Speech Communication最新文献

The speech intelligibility index (SII) and speech transmission index (STI) are widely accepted objective metrics for assessing speech intelligibility. In previous work, the relationship between STI and Chinese speech intelligibility (CSI) scores was studied. In this paper, the relationship between SII and CSI scores in rooms for the elderly aged 60–69 and over 70 is investigated by using auralization method under different background noise levels (40dBA and 55dBA) and different reverberation times. The results show that SII has good correlation with CSI score of the elderly. To get the same CSI score as the young adults, the elderly need a larger SII value, and the value increases with the increase of the age for the elderly. Since hearing loss of the elderly is considered in the calculation of SII, the difference in the required SII between the elderly and young is less than that of the required STI under the same CSI score condition. This indicates that SII is a more consistent evaluation criterion for different ages.

In this study, the challenges of adapting automatic speaker verification (ASV) systems to accommodate individuals with dysarthria, a speech disorder affecting intelligibility and articulation, are addressed. The scarcity of dysarthric speech data presents a significant obstacle in the development of an effective ASV system. To mitigate the detrimental effects of data paucity, an out-of-domain data augmentation approach was employed based on the observation that dysarthric speech often exhibits longer phoneme duration. Motivated by this observation, the duration of healthy speech data was modified with various stretching factors and then pooled into training, resulting in a significant reduction in the error rate. In addition to analyzing average phoneme duration, another analysis revealed that dysarthric speech contains crucial high-frequency spectral information. However, Mel-frequency cepstral coefficients (MFCC) are inherently designed to down-sample spectral information in the higher-frequency regions, and the same is true for Mel-filterbank features. To address this shortcoming, Linear-filterbank cepstral coefficients (LFCC) were used in combination with MFCC features. While MFCC effectively captures certain aspects of dysarthric speech, LFCC complements this by capturing high-frequency details essential for accurate dysarthric speaker verification. This proposed feature fusion effectively minimizes spectral information loss, further reducing error rates. To support the significance of combination of MFCC and LFCC features in an automatic speaker verification system for speakers with dysarthria, comprehensive experimentation was conducted. The fusion of MFCC and LFCC features was compared with several other front-end acoustic features, such as Mel-filterbank features, linear filterbank features, wavelet filterbank features, linear prediction cepstral coefficients (LPCC), frequency domain LPCC, and constant Q cepstral coefficients (CQCC). The approaches were evaluated using both i-vector and x-vector-based representation, comparing systems developed using MFCC and LFCC features individually and in combination. The experimental results presented in this paper demonstrate substantial improvements, with a 25.78% reduction in equal error rate (EER) for i-vector models and a 23.66% reduction in EER for x-vector models when compared to the baseline ASV system. Additionally, the effect of feature concatenation with variation in dysarthria severity levels (low, medium, and high) was studied, and the proposed approach was found to be highly effective in those cases as well.

In this article, a lightweight and interpretable speech intelligibility prediction network is proposed. It is based on the ESTOI metric with several extensions: learned modulation filterbank, temporal attention, and taking into account robustness of a given reference recording. The proposed network is differentiable, and therefore it can be applied as a loss function in speech enhancement systems. The method was evaluated using the Clarity Prediction Challenge dataset. Compared to MB-STOI, the best of the systems proposed in this paper reduced RMSE from 28.01 to 21.33. It also outperformed best performing systems from the Clarity Challenge, while its training does not require additional labels like speech enhancement system and talker. It also has small memory and requirements, therefore, it can be potentially used as a loss function to train speech enhancement system. As it would consume less resources, the saved ones can be used for a larger speech enhancement neural network.

Parkinson's Disease (PD) is a progressive neurodegenerative disorder affecting motor and non-motor symptoms. Its symptoms develop slowly, making early identification difficult. Machine learning has a significant potential to predict Parkinson's disease on features hidden in voice data. This work aimed to identify the most relevant features from a high-dimensional dataset, which helps accurately classify Parkinson's Disease with less computation time. Three individual datasets with various medical features based on voice have been analyzed in this work. An Ensemble Feature Selection Algorithm (EFSA) technique based on filter, wrapper, and embedding algorithms that pick highly relevant features for identifying Parkinson's Disease is proposed, and the same has been validated on three different datasets based on voice. These techniques can shorten training time to improve model accuracy and minimize overfitting. We utilized different ML models such as K-Nearest Neighbors (KNN), Random Forest, Decision Tree, Support Vector Machine (SVM), Bagging Classifier, Multi-Layer Perceptron (MLP) Classifier, and Gradient Boosting. Each of these models was fine-tuned to ensure optimal performance within our specific context. Moreover, in addition to these established classifiers, we proposed an ensemble classifier is found on a high optimal majority of the votes. Dataset-I achieves classification accuracy with 97.6 %, F₁-score 97.9 %, precision with 98 % and recall with 98 %. Dataset-II achieves classification accuracy 90.2 %, F₁-score 90.2 %, precision 90.2 %, and recall 90.5 %. Dataset-III achieves 83.3 % accuracy, F₁-score 83.3 %, precision 83.5 % and recall 83.3 %. These results have been taken using 13 out of 23, 45 out of 754, and 17 out of 46 features from respective datasets. The proposed EFSA model has performed with higher accuracy and is more efficient than other models for each dataset.

This study investigates conversational feedback, that is, a listener's reaction in response to a speaker, a phenomenon which occurs in all natural interactions. Feedback depends on the main speaker's productions and in return supports the elaboration of the interaction. As a consequence, feedback production has a direct impact on the quality of the interaction.

This paper examines all types of feedback, from generic to specific feedback, the latter of which has received less attention in the literature. We also present a fine-grained labeling system introducing two sub-types of specific feedback: positive/negative and given/new. Following a literature review on linguistic and machine learning perspectives highlighting the main issues in feedback prediction, we present a model based on a set of multimodal features which predicts the possible position of feedback and its type. This computational model makes it possible to precisely identify the different features in the speaker's production (morpho-syntactic, prosodic and mimo-gestural) which play a role in triggering feedback from the listener; the model also evaluates their relative importance.

The main contribution of this study is twofold: we sought to improve 1/ the model's performance in comparison with other approaches relying on a small set of features, and 2/ the model's interpretability, in particular by investigating feature importance. By integrating all the different modalities as well as high-level features, our model is uniquely positioned to be applied to French corpora.

This paper presents the newly developed non-native children’s English speech (NNCES) corpus to reveal the findings of automatic speaker and age recognition from raw speech. Convolutional neural networks (CNN), which have the ability to learn low-level speech representations, can be fed directly with raw speech signals instead of using traditional hand-crafted features. Moreover, the filters that were learned using standard CNNs appeared to be noisy because they consider all elements of each filter. In contrast, sincNet can be able to generate more meaningful filters simply by replacing the first convolutional layer by a sinc-layer in standard CNNs. The low and high cutoff frequencies of the rectangular band-pass filter are the only parameters that can be learned in sincNet, which has the potential to extract significant speech cues from the speaker, such as pitch and formants. In this work, the sincNet model is significantly changed by switching from baseline Mel scale initializations to equivalent rectangular bandwidth (ERB) initializations, which has the added benefit of allocating additional filters in the lower region of the spectrum. Additionally, it needs to be highlighted that the novel sincNet model is well suited to identify the age of the children. The investigations on both read and spontaneous speech tasks in speaker identification, gender independent & dependent age-group identification of children outperform the baseline models with varying relative improvements in terms of accuracy.

In this paper, we looked at the impact of musical experience on whistled vowel categorization by native French speakers. Whistled speech, a natural, yet modified speech type, augments speech amplitude while transposing the signal to a range of fairly high frequencies, i.e. 1 to 4 kHz. The whistled vowels are simple pitches of different heights depending on the vowel position, and generally represent the most stable part of the signal, just as in modal speech. They are modulated by consonant coarticulation(s), resulting in characteristic pitch movements. This change in speech mode can liken the speech signal to musical notes and their modulations; however, the mechanisms used to categorize whistled phonemes rely on abstract phonological knowledge and representation. Here we explore the impact of musical expertise on such a process by focusing on four whistled vowels (/i, e, a, o/) which have been used in previous experiments with non-musicians. We also included inter-speaker production variations, adding variability to the vowel pitches. Our results showed that all participants categorize whistled vowels well over chance, with musicians showing advantages for the middle whistled vowels (/a/ and /e/) as well as for the lower whistled vowel /o/. The whistler variability also affects musicians more than non-musicians and impacts their advantage, notably for the vowels /e/ and /o/. However, we find no specific training advantage for musicians over the whole experiment, but rather training effects for /a/ and /e/ when taking into account all participants. This suggests that though musical experience may help structure the vowel hierarchy when the whistler has a larger range, this advantage cannot be generalized when listening to another whistler. Thus, the transfer of musical knowledge present in this task only influences certain aspects of speech perception.

Weighted linear prediction (WLP) has demonstrated its significance in voice inverse filtering, contributing to enhanced methods for estimating both the vocal tract filter and the glottal source. WLP provides a mechanism to mitigate the effect on the linear prediction model of voice samples that affects the vocal tract filter estimation, particularly those samples around glottal closure instants (GCIs). This article studies the Gaussian weighted linear prediction (GLP) strategy, which employs a Gaussian attenuation window centered at the GCIs to reduce its contribution in the WLP analysis. In this study, the Gaussian attenuation is revisited and a parameterization of the window that adjusts to the typical variability in voice periodicity is introduced. In addition, an asymmetric Gaussian window is proposed to diminish the relevance of voice samples preceding GCIs on the WLP model, thus providing a quasi closed phase inverse filtering method. Characterization of symmetric and asymmetric GLP methods for glottal source estimation is addressed based on synthetic and natural phonation data, resulting in a set of optimal parameters for the Gaussian attenuation windows. The results show that the proposed asymmetric attenuation improves voice inverse filtering with respect to the symmetric GLP method. Comparisons with other state-of-the-art techniques suggest that the proposed GLP approaches are competitive, falling slightly short in performance only when contrasted with the well-known quasi closed inverse filtering analysis. The simplicity of implementing the attenuation windows, coupled with their robust performance, positions the proposed GLP methods as two attractive and straightforward voice inverse filtering techniques for practical application.

Data scarcity makes low-resource speech recognition systems suffer from severe overfitting. Although fine-tuning addresses this issue to some extent, it leads to parameter-inefficient training. In this paper, a novel language knowledge fusion method, named LanFusion, is proposed. It is built on the recent popular adapter-tuning technique, thus maintaining better parameter efficiency compared with conventional fine-tuning methods. LanFusion is a two-stage method. Specifically, multiple adapters are first trained on several source languages to extract language-specific and language-invariant knowledge. Then, the trained adapters are re-trained on the target low-resource language to fuse the learned knowledge. Compared with Vanilla-adapter, LanFusion obtains a relative average word error rate (WER) reduction of 9.8% and 8.6% on the Common Voice and FLEURS corpora, respectively. Extensive experiments demonstrate the proposed method is not only simple and effective but also parameter-efficient. Besides, using source languages that are geographically similar to the target language yields better results on both datasets.

This paper designs a weighted minimum mean square error (WMMSE) based distortionless convolution beamformer (DCBF) for joint dereverberation and denoising. By effectively using WMMSE with the constraint of distortionless, a DCBF is deduced, where the outputs of the weighted prediction error (WPE) filter and the WPE-based minimum variance distortionless response (MVDR) beamformer are combined to initialize target signal for balancing signal distortion, residual reverberation and residual noise. In addition, two optimization factors are introduced to reduce the reverberation and noise when the initialized target signal is used for the solution of beamformer. As a result, the designed beamformer is presented as a linear combination of the WMMSE-based convolution beamformer (CBF) and weighted power minimization distortionless response (WPD) filter. The experimental results demonstrate the superior performance of the designed beamformer for joint dereverberation and denoising compared to the reference methods.