Pub Date : 2016-12-01DOI: 10.1109/SLT.2016.7846255
Fumiya Shiozawa, D. Saito, N. Minematsu
The term of “World Englishes” describes the current state of English and one of their main characteristics is a large diversity of pronunciation, called accents. In our previous studies, we developed several techniques to realize effective clustering and visualization of the diversity. For this aim, the accent gap between two speakers has to be quantified independently of extra-linguistic factors such as age and gender. To realize this, a unique representation of speech, called speech structure, which is theoretically invariant against these factors, was applied to represent pronunciation. In the current study, by controlling the degree of invariance, we attempt to improve accent gap prediction. Two techniques are tested: DNN-based model-free estimation of divergence and multi-stream speech structures. In the former, instead of estimating separability between two speech events based on some model assumptions, DNN-based class posteriors are utilized for estimation. In the latter, by deriving one speech structure for each sub-space of acoustic features, constrained invariance is realized. Our proposals are tested in terms of the correlation between reference accent gaps and the predicted and quantified gaps. Experiments show that the correlation is improved from 0.718 to 0.730.
{"title":"Improved prediction of the accent gap between speakers of English for individual-based clustering of World Englishes","authors":"Fumiya Shiozawa, D. Saito, N. Minematsu","doi":"10.1109/SLT.2016.7846255","DOIUrl":"https://doi.org/10.1109/SLT.2016.7846255","url":null,"abstract":"The term of “World Englishes” describes the current state of English and one of their main characteristics is a large diversity of pronunciation, called accents. In our previous studies, we developed several techniques to realize effective clustering and visualization of the diversity. For this aim, the accent gap between two speakers has to be quantified independently of extra-linguistic factors such as age and gender. To realize this, a unique representation of speech, called speech structure, which is theoretically invariant against these factors, was applied to represent pronunciation. In the current study, by controlling the degree of invariance, we attempt to improve accent gap prediction. Two techniques are tested: DNN-based model-free estimation of divergence and multi-stream speech structures. In the former, instead of estimating separability between two speech events based on some model assumptions, DNN-based class posteriors are utilized for estimation. In the latter, by deriving one speech structure for each sub-space of acoustic features, constrained invariance is realized. Our proposals are tested in terms of the correlation between reference accent gaps and the predicted and quantified gaps. Experiments show that the correlation is improved from 0.718 to 0.730.","PeriodicalId":281635,"journal":{"name":"2016 IEEE Spoken Language Technology Workshop (SLT)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129088224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-12-01DOI: 10.1109/SLT.2016.7846240
Mortaza Doulaty, R. Rose, O. Siohan
Speech recognition performance using deep neural network based acoustic models is known to degrade when the acoustic environment and the speaker population in the target utterances are significantly different from the conditions represented in the training data. To address these mismatched scenarios, multi-style training (MTR) has been used to perturb utterances in an existing uncorrupted and potentially mismatched training speech corpus to better match target domain utterances. This paper addresses the problem of determining the distribution of perturbation levels for a given set of perturbation types that best matches the target speech utterances. An approach is presented that, given a small set of utterances from a target domain, automatically identifies an empirical distribution of perturbation levels that can be applied to utterances in an existing training set. Distributions are estimated for perturbation types that include acoustic background environments, reverberant room configurations, and speaker related variation like frequency and temporal warping. The end goal is for the resulting perturbed training set to characterize the variability in the target domain and thereby optimize ASR performance. An experimental study is performed to evaluate the impact of this approach on ASR performance when the target utterances are taken from a simulated far-field acoustic environment.
{"title":"Automatic optimization of data perturbation distributions for multi-style training in speech recognition","authors":"Mortaza Doulaty, R. Rose, O. Siohan","doi":"10.1109/SLT.2016.7846240","DOIUrl":"https://doi.org/10.1109/SLT.2016.7846240","url":null,"abstract":"Speech recognition performance using deep neural network based acoustic models is known to degrade when the acoustic environment and the speaker population in the target utterances are significantly different from the conditions represented in the training data. To address these mismatched scenarios, multi-style training (MTR) has been used to perturb utterances in an existing uncorrupted and potentially mismatched training speech corpus to better match target domain utterances. This paper addresses the problem of determining the distribution of perturbation levels for a given set of perturbation types that best matches the target speech utterances. An approach is presented that, given a small set of utterances from a target domain, automatically identifies an empirical distribution of perturbation levels that can be applied to utterances in an existing training set. Distributions are estimated for perturbation types that include acoustic background environments, reverberant room configurations, and speaker related variation like frequency and temporal warping. The end goal is for the resulting perturbed training set to characterize the variability in the target domain and thereby optimize ASR performance. An experimental study is performed to evaluate the impact of this approach on ASR performance when the target utterances are taken from a simulated far-field acoustic environment.","PeriodicalId":281635,"journal":{"name":"2016 IEEE Spoken Language Technology Workshop (SLT)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114923030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-12-01DOI: 10.1109/SLT.2016.7846269
Mohamed Bouaziz, Mohamed Morchid, Richard Dufour, G. Linarès
Most of the Natural and Spoken Language Processing tasks now employ Neural Networks (NN), allowing them to reach impressive performances. Embedding features allow the NLP systems to represent input vectors in a latent space and to improve the observed performances. In this context, Recurrent Neural Network (RNN) based architectures such as Long Short-Term Memory (LSTM) are well known for their capacity to encode sequential data into a non-sequential hidden vector representation, called sequence embedding. In this paper, we propose an LSTM-based multi-stream sequence embedding in order to encode parallel sequences by a single non-sequential latent representation vector. We then propose to map this embedding representation in a high-dimensional space using a Support Vector Machine (SVM) in order to classify the multi-stream sequences by finding out an optimal hyperplane. Multi-stream sequence embedding allowed the SVM classifier to more efficiently profit from information carried by both parallel streams and longer sequences. The system achieved the best performance, in a multi-stream sequence classification task, with a gain of 9 points in error rate compared to an SVM trained on the original input sequences.
{"title":"Improving multi-stream classification by mapping sequence-embedding in a high dimensional space","authors":"Mohamed Bouaziz, Mohamed Morchid, Richard Dufour, G. Linarès","doi":"10.1109/SLT.2016.7846269","DOIUrl":"https://doi.org/10.1109/SLT.2016.7846269","url":null,"abstract":"Most of the Natural and Spoken Language Processing tasks now employ Neural Networks (NN), allowing them to reach impressive performances. Embedding features allow the NLP systems to represent input vectors in a latent space and to improve the observed performances. In this context, Recurrent Neural Network (RNN) based architectures such as Long Short-Term Memory (LSTM) are well known for their capacity to encode sequential data into a non-sequential hidden vector representation, called sequence embedding. In this paper, we propose an LSTM-based multi-stream sequence embedding in order to encode parallel sequences by a single non-sequential latent representation vector. We then propose to map this embedding representation in a high-dimensional space using a Support Vector Machine (SVM) in order to classify the multi-stream sequences by finding out an optimal hyperplane. Multi-stream sequence embedding allowed the SVM classifier to more efficiently profit from information carried by both parallel streams and longer sequences. The system achieved the best performance, in a multi-stream sequence classification task, with a gain of 9 points in error rate compared to an SVM trained on the original input sequences.","PeriodicalId":281635,"journal":{"name":"2016 IEEE Spoken Language Technology Workshop (SLT)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114891322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-12-01DOI: 10.1109/SLT.2016.7846289
Killian Janod, Mohamed Morchid, Richard Dufour, G. Linarès
This paper proposes an original method which integrates contextual information of words into Word2vec neural networks that learn from words and their respective context windows. In the classical word embedding approach, context windows are represented as bag-of-words, i.e. every word in the context is treated equally. A log-linear weighting approach modeling the continuous context is proposed in our model to take into account the relative position of words in the surrounding context of the word. Quality improvements implied by this method are shown on the the Semantic-Syntactic Word Relationship test and on a real application framework implying a theme identification task of human dialogues. The promising gains of our adapted Word2vec model of 7 and 5 points for Skip-gram and CBOW approaches respectively demonstrate that the proposed models are a step forward for word and document representation.
{"title":"A log-linear weighting approach in the Word2vec space for spoken language understanding","authors":"Killian Janod, Mohamed Morchid, Richard Dufour, G. Linarès","doi":"10.1109/SLT.2016.7846289","DOIUrl":"https://doi.org/10.1109/SLT.2016.7846289","url":null,"abstract":"This paper proposes an original method which integrates contextual information of words into Word2vec neural networks that learn from words and their respective context windows. In the classical word embedding approach, context windows are represented as bag-of-words, i.e. every word in the context is treated equally. A log-linear weighting approach modeling the continuous context is proposed in our model to take into account the relative position of words in the surrounding context of the word. Quality improvements implied by this method are shown on the the Semantic-Syntactic Word Relationship test and on a real application framework implying a theme identification task of human dialogues. The promising gains of our adapted Word2vec model of 7 and 5 points for Skip-gram and CBOW approaches respectively demonstrate that the proposed models are a step forward for word and document representation.","PeriodicalId":281635,"journal":{"name":"2016 IEEE Spoken Language Technology Workshop (SLT)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131889255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-12-01DOI: 10.1109/SLT.2016.7846336
Florian Hinterleitner, Benjamin Weiss, S. Möller
State-of-the-art approaches on text-to-speech (TTS) synthesis like unit selection and HMM synthesis are data-driven. Therefore, they use a prerecorded speech corpus of natural speech to build a voice. This paper investigates the influence of the size of the speech corpus on five different perceptual quality dimensions. Six German unit selection voices were created based on subsets of different sizes of the same speech corpus using the MaryTTS synthesis platform. Statistical analysis showed a significant influence of the size of the speech corpus on all of the five dimensions. Surprisingly the voice created from the second largest speech corpus reached the best ratings in almost all dimensions, with the rating in the dimension fluency and intelligibility being significantly higher than the ratings of any other voice. Moreover, we could also verify a significant effect of the synthesized utterance on four of the five perceptual quality dimensions.
{"title":"Influence of corpus size and content on the perceptual quality of a unit selection MaryTTS voice","authors":"Florian Hinterleitner, Benjamin Weiss, S. Möller","doi":"10.1109/SLT.2016.7846336","DOIUrl":"https://doi.org/10.1109/SLT.2016.7846336","url":null,"abstract":"State-of-the-art approaches on text-to-speech (TTS) synthesis like unit selection and HMM synthesis are data-driven. Therefore, they use a prerecorded speech corpus of natural speech to build a voice. This paper investigates the influence of the size of the speech corpus on five different perceptual quality dimensions. Six German unit selection voices were created based on subsets of different sizes of the same speech corpus using the MaryTTS synthesis platform. Statistical analysis showed a significant influence of the size of the speech corpus on all of the five dimensions. Surprisingly the voice created from the second largest speech corpus reached the best ratings in almost all dimensions, with the rating in the dimension fluency and intelligibility being significantly higher than the ratings of any other voice. Moreover, we could also verify a significant effect of the synthesized utterance on four of the five perceptual quality dimensions.","PeriodicalId":281635,"journal":{"name":"2016 IEEE Spoken Language Technology Workshop (SLT)","volume":"11 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114039200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-12-01DOI: 10.1109/SLT.2016.7846254
Xinhao Wang, Keelan Evanini, James V. Bruno, Matthew David Mulholland
This paper addresses the task of automatically detecting plagiarized responses in the context of a test of spoken English proficiency for non-native speakers. Text-to-text content similarity features are used jointly with speaking proficiency features extracted using an automated speech scoring system to train classifiers to distinguish between plagiarized and non-plagiarized spoken responses. A large data set drawn from an operational English proficiency assessment is used to simulate the performance of the detection system in a practical application. The best classifier on this heavily imbalanced data set resulted in an F1-score of 0.706 on the plagiarized class. These results indicate that the proposed system can potentially be used to improve the validity of both human and automated assessment of non-native spoken English.
{"title":"Automatic plagiarism detection for spoken responses in an assessment of English language proficiency","authors":"Xinhao Wang, Keelan Evanini, James V. Bruno, Matthew David Mulholland","doi":"10.1109/SLT.2016.7846254","DOIUrl":"https://doi.org/10.1109/SLT.2016.7846254","url":null,"abstract":"This paper addresses the task of automatically detecting plagiarized responses in the context of a test of spoken English proficiency for non-native speakers. Text-to-text content similarity features are used jointly with speaking proficiency features extracted using an automated speech scoring system to train classifiers to distinguish between plagiarized and non-plagiarized spoken responses. A large data set drawn from an operational English proficiency assessment is used to simulate the performance of the detection system in a practical application. The best classifier on this heavily imbalanced data set resulted in an F1-score of 0.706 on the plagiarized class. These results indicate that the proposed system can potentially be used to improve the validity of both human and automated assessment of non-native spoken English.","PeriodicalId":281635,"journal":{"name":"2016 IEEE Spoken Language Technology Workshop (SLT)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129321592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-12-01DOI: 10.1109/SLT.2016.7846291
Takeshi Homma, Kazuaki Shima, Takuya Matsumoto
In order to achieve an utterance classifier that not only works robustly against speech recognition errors but also maintains high accuracy for input with no errors, we propose the following techniques. First, we propose a classifier training method in which not only error-free transcriptions but also recognized sentences with errors were used as training data. To maintain high accuracy whether or not input has recognition errors, we adjusted a scaling factor of the number of transcriptions for training data. Second, we introduced three classifiers that utilize different input features: words, phonemes, and words recovered from phonetic recognition errors. We also introduced a selection method that selects the most probable utterance class from outputs of multiple utterance classifiers using recognition results obtained from enhanced and non-enhanced speech signals. Experimental results showed our method cuts 55% of classification errors for speech recognition input while accuracy degradation rate for transcription input is 0.7%.
{"title":"Robust utterance classification using multiple classifiers in the presence of speech recognition errors","authors":"Takeshi Homma, Kazuaki Shima, Takuya Matsumoto","doi":"10.1109/SLT.2016.7846291","DOIUrl":"https://doi.org/10.1109/SLT.2016.7846291","url":null,"abstract":"In order to achieve an utterance classifier that not only works robustly against speech recognition errors but also maintains high accuracy for input with no errors, we propose the following techniques. First, we propose a classifier training method in which not only error-free transcriptions but also recognized sentences with errors were used as training data. To maintain high accuracy whether or not input has recognition errors, we adjusted a scaling factor of the number of transcriptions for training data. Second, we introduced three classifiers that utilize different input features: words, phonemes, and words recovered from phonetic recognition errors. We also introduced a selection method that selects the most probable utterance class from outputs of multiple utterance classifiers using recognition results obtained from enhanced and non-enhanced speech signals. Experimental results showed our method cuts 55% of classification errors for speech recognition input while accuracy degradation rate for transcription input is 0.7%.","PeriodicalId":281635,"journal":{"name":"2016 IEEE Spoken Language Technology Workshop (SLT)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129136619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-12-01DOI: 10.1109/SLT.2016.7846272
Pierre Lison, R. Meena
Movie and TV subtitles contain large amounts of conversational material, but lack an explicit turn structure. This paper present a data-driven approach to the segmentation of subtitles into dialogue turns. Training data is first extracted by aligning subtitles with transcripts in order to obtain speaker labels. This data is then used to build a classifier whose task is to determine whether two consecutive sentences are part of the same dialogue turn. The approach relies on linguistic, visual and timing features extracted from the subtitles themselves and does not require access to the audiovisual material - although speaker diarization can be exploited when audio data is available. The approach also exploits alignments with related subtitles in other languages to further improve the classification performance. The classifier achieves an accuracy of 78 % on a held-out test set. A follow-up annotation experiment demonstrates that this task is also difficult for human annotators.
{"title":"Automatic turn segmentation for Movie & TV subtitles","authors":"Pierre Lison, R. Meena","doi":"10.1109/SLT.2016.7846272","DOIUrl":"https://doi.org/10.1109/SLT.2016.7846272","url":null,"abstract":"Movie and TV subtitles contain large amounts of conversational material, but lack an explicit turn structure. This paper present a data-driven approach to the segmentation of subtitles into dialogue turns. Training data is first extracted by aligning subtitles with transcripts in order to obtain speaker labels. This data is then used to build a classifier whose task is to determine whether two consecutive sentences are part of the same dialogue turn. The approach relies on linguistic, visual and timing features extracted from the subtitles themselves and does not require access to the audiovisual material - although speaker diarization can be exploited when audio data is available. The approach also exploits alignments with related subtitles in other languages to further improve the classification performance. The classifier achieves an accuracy of 78 % on a held-out test set. A follow-up annotation experiment demonstrates that this task is also difficult for human annotators.","PeriodicalId":281635,"journal":{"name":"2016 IEEE Spoken Language Technology Workshop (SLT)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117240134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-12-01DOI: 10.1109/SLT.2016.7846261
Shi-Xiong Zhang, Zhuo Chen, Yong Zhao, Jinyu Li, Y. Gong
A new type of End-to-End system for text-dependent speaker verification is presented in this paper. Previously, using the phonetic discriminate/speaker discriminate DNN as a feature extractor for speaker verification has shown promising results. The extracted frame-level (bottleneck, posterior or d-vector) features are equally weighted and aggregated to compute an utterance-level speaker representation (d-vector or i-vector). In this work we use a speaker discriminate CNN to extract the noise-robust frame-level features. These features are smartly combined to form an utterance-level speaker vector through an attention mechanism. The proposed attention model takes the speaker discriminate information and the phonetic information to learn the weights. The whole system, including the CNN and attention model, is joint optimized using an end-to-end criterion. The training algorithm imitates exactly the evaluation process — directly mapping a test utterance and a few target speaker utterances into a single verification score. The algorithm can smartly select the most similar impostor for each target speaker to train the network. We demonstrated the effectiveness of the proposed end-to-end system on Windows 10 “Hey Cortana” speaker verification task.
{"title":"End-to-End attention based text-dependent speaker verification","authors":"Shi-Xiong Zhang, Zhuo Chen, Yong Zhao, Jinyu Li, Y. Gong","doi":"10.1109/SLT.2016.7846261","DOIUrl":"https://doi.org/10.1109/SLT.2016.7846261","url":null,"abstract":"A new type of End-to-End system for text-dependent speaker verification is presented in this paper. Previously, using the phonetic discriminate/speaker discriminate DNN as a feature extractor for speaker verification has shown promising results. The extracted frame-level (bottleneck, posterior or d-vector) features are equally weighted and aggregated to compute an utterance-level speaker representation (d-vector or i-vector). In this work we use a speaker discriminate CNN to extract the noise-robust frame-level features. These features are smartly combined to form an utterance-level speaker vector through an attention mechanism. The proposed attention model takes the speaker discriminate information and the phonetic information to learn the weights. The whole system, including the CNN and attention model, is joint optimized using an end-to-end criterion. The training algorithm imitates exactly the evaluation process — directly mapping a test utterance and a few target speaker utterances into a single verification score. The algorithm can smartly select the most similar impostor for each target speaker to train the network. We demonstrated the effectiveness of the proposed end-to-end system on Windows 10 “Hey Cortana” speaker verification task.","PeriodicalId":281635,"journal":{"name":"2016 IEEE Spoken Language Technology Workshop (SLT)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114651716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-12-01DOI: 10.1109/SLT.2016.7846327
Vipul Arora, A. Lahiri, Henning Reetz
Deep neural network (DNN) acoustic models can be adapted to under-resourced languages by transferring the hidden layers. An analogous transfer problem is popular as few-shot learning to recognise scantily seen objects based on their meaningful attributes. In similar way, this paper proposes a principled way to represent the hidden layers of DNN in terms of attributes shared across languages. The diverse phoneme sets of different languages can be represented in terms of phonological features that are shared by them. The DNN layers estimating these features could then be transferred in a meaningful and reliable way. Here, we evaluate model transfer from English to German, by comparing the proposed method with other popular methods on the task of phoneme recognition. Experimental results support that apart from providing interpretability to the DNN acoustic models, the proposed framework provides efficient means for their speedy adaptation to different languages, even in the face of scanty adaptation data.
{"title":"Attribute based shared hidden layers for cross-language knowledge transfer","authors":"Vipul Arora, A. Lahiri, Henning Reetz","doi":"10.1109/SLT.2016.7846327","DOIUrl":"https://doi.org/10.1109/SLT.2016.7846327","url":null,"abstract":"Deep neural network (DNN) acoustic models can be adapted to under-resourced languages by transferring the hidden layers. An analogous transfer problem is popular as few-shot learning to recognise scantily seen objects based on their meaningful attributes. In similar way, this paper proposes a principled way to represent the hidden layers of DNN in terms of attributes shared across languages. The diverse phoneme sets of different languages can be represented in terms of phonological features that are shared by them. The DNN layers estimating these features could then be transferred in a meaningful and reliable way. Here, we evaluate model transfer from English to German, by comparing the proposed method with other popular methods on the task of phoneme recognition. Experimental results support that apart from providing interpretability to the DNN acoustic models, the proposed framework provides efficient means for their speedy adaptation to different languages, even in the face of scanty adaptation data.","PeriodicalId":281635,"journal":{"name":"2016 IEEE Spoken Language Technology Workshop (SLT)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125596040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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