Speech Communication最新文献

The current digital speech deletion and insertion tampering detection methods mainly employes the extraction of phase and frequency features of the Electrical Network Frequency (ENF). However, there are some problems with the existing approaches, such as the alignment problem for speech samples with different durations, the sparsity of ENF features, and the small number of tampered speech samples for training, which lead to low accuracy of deletion and insertion tampering detection. Therefore, this paper proposes a tampering detection method for digital speech deletion and insertion based on ENF Fluctuation Super-vector (ENF-FSV) and deep feature learning representation. By extracting the parameters of ENF phase and frequency fitting curves, feature alignment and dimensionality reduction are achieved, and the alignment and sparsity problems are avoided while the fluctuation information of phase and frequency is extracted. To solve the problem of the insufficient sample size of tampered speech for training, the ENF Universal Background Model (ENF-UBM) is built by a large number of untampered speech samples, and the mean vector is updated to extract ENF-FSV. Considering the shallow representation of ENF features with not highlighting important features, we construct an end-to-end deep neural network to strengthen the attention to the abrupt fluctuation information by the attention mechanism to enhance the representational power of the ENF-FSV features, and then the deep ENF-FSV features extracted by the Residual Network (ResNet) module are fed to the designed classification network for tampering detection. The experimental results show that the method in this paper exhibits higher accuracy and better robustness in the Carioca, New Spanish, and ENF High-sampling Group (ENF-HG) databases when compared with the state-of-the-art methods.

The main goal of this paper is to improve our insight in the mental preparation of speech, based on speakers' self-monitoring behavior. To this end we re-analyze the aggregated responses from earlier published experiments eliciting speech sound errors. The re-analyses confirm or show that (1) “early” and “late” detections of elicited speech sound errors can be distinguished, with a time delay in the order of 500 ms; (2) a main cause for some errors to be detected “early”, others “late” and others again not at all is the size of the phonetic contrast between the error and the target speech sound; (3) repairs of speech sound errors stem from competing (and sometimes active) word candidates. These findings lead to some speculative conclusions regarding the mental preparation of speech. First, there are two successive stages of mental preparation, an “early” and a “late” stage. Second, at the “early” stage of speech preparation, speech sounds are represented as targets in auditory perceptual space, at the “late” stage as coordinated motor commands necessary for articulation. Third, repairs of speech sound errors stem from response candidates competing for the same slot with the error form, and some activation often is sustained until after articulation.

In this work, we tested different variants of a Forensic Automatic Speaker Recognition (FASR) system based on Emphasized Channel Attention, Propagation and Aggregation in Time Delay Neural Network (ECAPA-TDNN). To this scope, conditions reflecting those of a real forensic voice comparison case have been taken into consideration according to the forensic_eval_01 evaluation campaign settings. Using this recent neural model as an embedding extraction block, various normalization strategies at the level of embeddings and scores allowed us to observe the variations in system performance in terms of discriminating power, accuracy and precision metrics. Our findings suggest that the ECAPA-TDNN can be successfully used as a base component of a FASR system, managing to surpass the previous state of the art, at least in the context of the considered operating conditions.

Neurological voice disorders are caused by problems in the nervous system as it interacts with the larynx. In this paper, we propose to use wavelet scattering transform (WST)-based features in automatic classification of neurological voice disorders. As a part of WST, a speech signal is processed in stages with each stage consisting of three operations – convolution, modulus and averaging – to generate low-variance data representations that preserve discriminability across classes while minimizing differences within a class. The proposed WST-based features were extracted from speech signals of patients suffering from either spasmodic dysphonia (SD) or recurrent laryngeal nerve palsy (RLNP) and from speech signals of healthy speakers of the Saarbruecken voice disorder (SVD) database. Two machine learning algorithms (support vector machine (SVM) and feed forward neural network (NN)) were trained separately using the WST-based features, to perform two binary classification tasks (healthy vs. SD and healthy vs. RLNP) and one multi-class classification task (healthy vs. SD vs. RLNP). The results show that WST-based features outperformed state-of-the-art features in all three tasks. Furthermore, the best overall classification performance was achieved by the NN classifier trained using WST-based features.

Vocal intensity, which is quantified typically with the sound pressure level (SPL), is a key feature of speech. To measure SPL from speech recordings, a standard calibration tone (with a reference SPL of 94 dB or 114 dB) needs to be recorded together with speech. However, most of the popular databases that are used in areas such as speech and speaker recognition have been recorded without calibration information by expressing speech on arbitrary amplitude scales. Therefore, information about vocal intensity of the recorded speech, including SPL, is lost. In the current study, we introduce a new open and calibrated speech/electroglottography (EGG) database named Aalto Vocal Intensity Database (AVID). AVID includes speech and EGG produced by 50 speakers (25 males, 25 females) who varied their vocal intensity in four categories (soft, normal, loud and very loud). Recordings were conducted using a constant mouth-to-microphone distance and by recording a calibration tone. The speech data was labelled sentence-wise using a total of 19 labels that support the utilisation of the data in machine learning (ML) -based studies of vocal intensity based on supervised learning. In order to demonstrate how the AVID data can be used to study vocal intensity, we investigated one multi-class classification task (classification of speech into soft, normal, loud and very loud intensity classes) and one regression task (prediction of SPL of speech). In both tasks, we deliberately warped the level of the input speech by normalising the signal to have its maximum amplitude equal to 1.0, that is, we simulated a scenario that is prevalent in current speech databases. The results show that using the spectrogram feature with the support vector machine classifier gave an accuracy of 82% in the multi-class classification of the vocal intensity category. In the prediction of SPL, using the spectrogram feature with the support vector regressor gave an mean absolute error of about 2 dB and a coefficient of determination of 92%. We welcome researchers interested in classification and regression problems to utilise AVID in the study of vocal intensity, and we hope that the current results could serve as baselines for future ML studies on the topic.

German primary diphthongs are conventionally transcribed using the same symbols used for some monophthong vowels. However, if the corresponding vocal tract shapes are used for articulatory synthesis, the results often sound unnatural. Furthermore, there is no clear consensus in the literature if diphthongs have monopthong constituents and if so, which ones. This study therefore analyzed a set of audio recordings from the reference speaker of the state-of-the-art articulatory synthesizer VocalTractLab to identify likely candidates for the monophthong constituents of the German primary diphthongs. We then evaluated these candidates in a listening experiment with naive listeners to determine a naturalness ranking of these candidates and specialized diphthong shapes. The results showed that the German primary diphthongs can indeed be synthesized with no significant loss in naturalness by replacing the specialized diphthong shapes for the initial and final segments by shapes also used for monopthong vowels.

The present study examines the impact of Arab speakers’ phonological and prosodic features on the accuracy of automatic speech recognition (ASR) of non-native English speech. The authors first investigated the perceptions of 30 Egyptian ESL teachers and 70 Egyptian university students towards the L1 (Arabic)-based errors affecting intelligibility and then carried out a data analysis of the ASR of the students’ English speech to find out whether the errors investigated resulted in intelligibility breakdowns in an ASR setting. In terms of the phonological features of non-native speech, the results showed that the teachers gave more weight to pronunciation features of accented speech that did not actually hinder recognition, that the students were mostly oblivious to the L2 errors they made and their impact on intelligibility, and that L2 errors which were not perceived as serious by both teachers and students had negative impacts on ASR accuracy levels. In regard to the prosodic features of non-native speech, it was found that lower speech rates resulted in more accurate speech recognition levels, higher speech intensity led to less deletion errors, and voice pitch did not seem to have any impact on ASR accuracy levels. The study, accordingly, recommends training ASR systems with more non-native data to increase their accuracy levels as well as paying more attention to remedying non-native speakers’ L1-based errors that are more likely to impact non-native automatic speech recognition.

Deep clustering is a popular unsupervised technique for feature representation learning. We recently proposed the chunk-based DeepEmoCluster framework for speech emotion recognition (SER) to adopt the concept of deep clustering as a novel semi-supervised learning (SSL) framework, which achieved improved recognition performances over conventional reconstruction-based approaches. However, the vanilla DeepEmoCluster lacks critical sentence-level temporal information that is useful for SER tasks. This study builds upon the DeepEmoCluster framework, creating a powerful SSL approach that leverages temporal information within a sentence. We propose two sentence-level temporal modeling alternatives using either the temporal-net or the triplet loss function, resulting in a novel temporal-enhanced DeepEmoCluster framework to capture essential temporal information. The key contribution to achieving this goal is the proposed sentence-level uniform sampling strategy, which preserves the original temporal order of the data for the clustering process. An extra network module (e.g., gated recurrent unit) is utilized for the temporal-net option to encode temporal information across the data chunks. Alternatively, we can impose additional temporal constraints by using the triplet loss function while training the DeepEmoCluster framework, which does not increase model complexity. Our experimental results based on the MSP-Podcast corpus demonstrate that the proposed temporal-enhanced framework significantly outperforms the vanilla DeepEmoCluster framework and other existing SSL approaches in regression tasks for the emotional attributes arousal, dominance, and valence. The improvements are observed in fully-supervised learning or SSL implementations. Further analyses validate the effectiveness of the proposed temporal modeling, showing (1) high temporal consistency in the cluster assignment, and (2) well-separated emotional patterns in the generated clusters.

Researchers have shown a growing interest in Audio-driven Talking Head Generation. The primary challenge in talking head generation is achieving audio-visual coherence between the lips and the audio, known as lip synchronization. This paper proposes a generic method, LPIPS-AttnWav2Lip, for reconstructing face images of any speaker based on audio. We used the U-Net architecture based on residual CBAM to better encode and fuse audio and visual modal information. Additionally, the semantic alignment module extends the receptive field of the generator network to obtain the spatial and channel information of the visual features efficiently; and match statistical information of visual features with audio latent vector to achieve the adjustment and injection of the audio content information to the visual information. To achieve exact lip synchronization and to generate realistic high-quality images, our approach adopts LPIPS Loss, which simulates human judgment of image quality and reduces instability possibility during the training process. The proposed method achieves outstanding performance in terms of lip synchronization accuracy and visual quality as demonstrated by subjective and objective evaluation results.

Deep learning has revolutionized voice activity detection (VAD) by offering promising solutions. However, directly applying traditional features, such as raw waveforms and Mel-frequency cepstral coefficients, to deep neural networks often leads to degraded VAD performance due to noise interference. In contrast, humans possess the remarkable ability to discern speech in complex and noisy environments, which motivated us to draw inspiration from the human auditory system. We propose a robust VAD algorithm called auditory-inspired masked modulation encoder based convolutional attention network (AMME-CANet) that integrates our AMME with CANet. Firstly, we investigate the design of auditory-inspired modulation features as a deep-learning encoder (AME), effectively simulating the process of sound-signal transmission to inner ear hair cells and subsequent modulation filtering by neural cells. Secondly, building upon the observed masking effects in the human auditory system, we enhance our auditory-inspired modulation encoder by incorporating a masking mechanism resulting in the AMME. The AMME amplifies cleaner speech frequencies while suppressing noise components. Thirdly, inspired by the human auditory mechanism and capitalizing on contextual information, we leverage the attention mechanism for VAD. This methodology uses an attention mechanism to assign higher weights to contextual information containing richer and more informative cues. Through extensive experimentation and evaluation, we demonstrated the superior performance of AMME-CANet in enhancing VAD under challenging noise conditions.