106 research outputs found
Exploiting Hidden Representations from a DNN-based Speech Recogniser for Speech Intelligibility Prediction in Hearing-impaired Listeners
An accurate objective speech intelligibility prediction algorithms is of
great interest for many applications such as speech enhancement for hearing
aids. Most algorithms measures the signal-to-noise ratios or correlations
between the acoustic features of clean reference signals and degraded signals.
However, these hand-picked acoustic features are usually not explicitly
correlated with recognition. Meanwhile, deep neural network (DNN) based
automatic speech recogniser (ASR) is approaching human performance in some
speech recognition tasks. This work leverages the hidden representations from
DNN-based ASR as features for speech intelligibility prediction in
hearing-impaired listeners. The experiments based on a hearing aid
intelligibility database show that the proposed method could make better
prediction than a widely used short-time objective intelligibility (STOI) based
binaural measure.Comment: Submitted to INTERSPEECH202
Automatic Pronunciation Assessment -- A Review
Pronunciation assessment and its application in computer-aided pronunciation
training (CAPT) have seen impressive progress in recent years. With the rapid
growth in language processing and deep learning over the past few years, there
is a need for an updated review. In this paper, we review methods employed in
pronunciation assessment for both phonemic and prosodic. We categorize the main
challenges observed in prominent research trends, and highlight existing
limitations, and available resources. This is followed by a discussion of the
remaining challenges and possible directions for future work.Comment: 9 pages, accepted to EMNLP Finding
Direct Speech Reconstruction From Articulatory Sensor Data by Machine Learning
This paper describes a technique that generates speech acoustics from articulator movements. Our motivation is to help people who can no longer speak following laryngectomy, a procedure that is carried out tens of thousands of times per year in the Western world. Our method for sensing articulator movement, permanent magnetic articulography, relies on small, unobtrusive magnets attached to the lips and tongue. Changes in magnetic field caused by magnet movements are sensed and form the input to a process that is trained to estimate speech acoustics. In the experiments reported here this “Direct Synthesis” technique is developed for normal speakers, with glued-on magnets, allowing us to train with parallel sensor and acoustic data. We describe three machine learning techniques for this task, based on Gaussian mixture models, deep neural networks, and recurrent neural networks (RNNs). We evaluate our techniques with objective acoustic distortion measures and subjective listening tests over spoken sentences read from novels (the CMU Arctic corpus). Our results show that the best performing technique is a bidirectional RNN (BiRNN), which employs both past and future contexts to predict the acoustics from the sensor data. BiRNNs are not suitable for synthesis in real time but fixed-lag RNNs give similar results and, because they only look a little way into the future, overcome this problem. Listening tests show that the speech produced by this method has a natural quality that preserves the identity of the speaker. Furthermore, we obtain up to 92% intelligibility on the challenging CMU Arctic material. To our knowledge, these are the best results obtained for a silent-speech system without a restricted vocabulary and with an unobtrusive device that delivers audio in close to real time. This work promises to lead to a technology that truly will give people whose larynx has been removed their voices back
Automated assessment of second language comprehensibility: Review, training, validation, and generalization studies
Whereas many scholars have emphasized the relative importance of comprehensibility as an ecologically valid goal for L2 speech training, testing, and development, eliciting listeners’ judgments is time-consuming. Following calls for research on more efficient L2 speech rating methods in applied linguistics, and growing attention toward using machine learning on spontaneous unscripted speech in speech engineering, the current study examined the possibility of establishing quick and reliable automated comprehensibility assessments. Orchestrating a set of phonological (maximum posterior probabilities and gaps between L1 and L2 speech), prosodic (pitch and intensity variation), and temporal measures (articulation rate, pause frequency), the regression model significantly predicted how naïve listeners intuitively judged low, mid, high, and nativelike comprehensibility among 100 L1 and L2 speakers’ picture descriptions. The strength of the correlation (r = .823 for machine vs. human ratings) was comparable to naïve listeners’ interrater agreement (r = .760 for humans vs. humans). The findings were successfully replicated when the model was applied to a new dataset of 45 L1 and L2 speakers (r = .827) and tested under a more freely constructed interview task condition (r = .809)
Deep Learning-based Speech Enhancement for Real-life Applications
Speech enhancement is the process of improving speech quality and intelligibility by suppressing noise. Inspired by the outstanding performance of the deep learning approach for speech enhancement, this thesis aims to add to this research area through the following contributions. The thesis presents an experimental analysis of different deep neural networks for speech enhancement, to compare their performance and investigate factors and approaches that improve the performance. The outcomes of this analysis facilitate the development of better speech enhancement networks in this work.
Moreover, this thesis proposes a new deep convolutional denoising autoencoderbased speech enhancement architecture, in which strided and dilated convolutions were applied to improve the performance while keeping network complexity to a minimum. Furthermore, a two-stage speech enhancement approach is proposed that reduces distortion, by performing a speech denoising first stage in the frequency domain, followed by a second speech reconstruction stage in the time domain. This approach was proven to reduce speech distortion, leading to better overall quality of the processed speech in comparison to state-of-the-art speech enhancement models.
Finally, the work presents two deep neural network speech enhancement architectures for hearing aids and automatic speech recognition, as two real-world speech enhancement applications. A smart speech enhancement architecture was proposed for hearing aids, which is an integrated hearing aid and alert system. This architecture enhances both speech and important emergency noise, and only eliminates undesired noise. The results show that this idea is applicable to improve the performance of hearing aids. On the other hand, the architecture proposed for automatic speech recognition solves the mismatch issue between speech enhancement automatic speech recognition systems, leading to significant reduction in the word error rate of a baseline automatic speech recognition system, provided by Intelligent Voice for research purposes. In conclusion, the results presented in this thesis show promising performance for the proposed architectures for real time speech enhancement applications
Deep Learning for Audio Signal Processing
Given the recent surge in developments of deep learning, this article
provides a review of the state-of-the-art deep learning techniques for audio
signal processing. Speech, music, and environmental sound processing are
considered side-by-side, in order to point out similarities and differences
between the domains, highlighting general methods, problems, key references,
and potential for cross-fertilization between areas. The dominant feature
representations (in particular, log-mel spectra and raw waveform) and deep
learning models are reviewed, including convolutional neural networks, variants
of the long short-term memory architecture, as well as more audio-specific
neural network models. Subsequently, prominent deep learning application areas
are covered, i.e. audio recognition (automatic speech recognition, music
information retrieval, environmental sound detection, localization and
tracking) and synthesis and transformation (source separation, audio
enhancement, generative models for speech, sound, and music synthesis).
Finally, key issues and future questions regarding deep learning applied to
audio signal processing are identified.Comment: 15 pages, 2 pdf figure
Recurrent neural networks for multi-microphone speech separation
This thesis takes the classical signal processing problem of separating the speech of a target speaker from a real-world audio recording containing noise, background interference — from competing speech or other non-speech sources —, and reverberation, and seeks data-driven solutions based on supervised learning methods, particularly recurrent neural networks (RNNs). Such speech separation methods can inject robustness in automatic speech recognition (ASR) systems and have been an active area of research for the past two decades. We particularly focus on applications where multi-channel recordings are available.
Stand-alone beamformers cannot simultaneously suppress diffuse-noise and protect the desired signal from any distortions. Post-filters complement the beamformers in obtaining the minimum mean squared error (MMSE) estimate of the desired signal. Time-frequency (TF) masking — a method having roots in computational auditory scene analysis (CASA) — is a suitable candidate for post-filtering, but the challenge lies in estimating the TF masks. The use of RNNs — in particular the bi-directional long short-term memory (BLSTM) architecture — as a post-filter estimating TF masks for a delay-and-sum beamformer (DSB) — using magnitude spectral and phase-based features — is proposed.
The data—recorded in 4 challenging realistic environments—from the CHiME-3 challenge is used. Two different TF masks — Wiener filter and log-ratio — are identified as suitable targets for learning. The separated speech is evaluated based on objective speech intelligibility measures: short-term objective intelligibility (STOI) and frequency-weighted segmental SNR (fwSNR). The word error rates (WERs) as reported by the previous state-of-the-art ASR back-end — when fed with the test data of the CHiME-3 challenge — are interpreted against the objective scores for understanding the relationships of the latter with the former. Overall, a consistent improvement in the objective scores brought in by the RNNs is observed compared to that of feed-forward neural networks and a baseline MVDR beamformer
Oesophageal speech: enrichment and evaluations
167 p.After a laryngectomy (i.e. removal of the larynx) a patient can no more speak in a healthy laryngeal voice. Therefore, they need to adopt alternative methods of speaking such as oesophageal speech. In this method, speech is produced using swallowed air and the vibrations of the pharyngo-oesophageal segment, which introduces several undesired artefacts and an abnormal fundamental frequency. This makes oesophageal speech processing difficult compared to healthy speech, both auditory processing and signal processing. The aim of this thesis is to find solutions to make oesophageal speech signals easier to process, and to evaluate these solutions by exploring a wide range of evaluation metrics.First, some preliminary studies were performed to compare oesophageal speech and healthy speech. This revealed significantly lower intelligibility and higher listening effort for oesophageal speech compared to healthy speech. Intelligibility scores were comparable for familiar and non-familiar listeners of oesophageal speech. However, listeners familiar with oesophageal speech reported less effort compared to non-familiar listeners. In another experiment, oesophageal speech was reported to have more listening effort compared to healthy speech even though its intelligibility was comparable to healthy speech. On investigating neural correlates of listening effort (i.e. alpha power) using electroencephalography, a higher alpha power was observed for oesophageal speech compared to healthy speech, indicating higher listening effort. Additionally, participants with poorer cognitive abilities (i.e. working memory capacity) showed higher alpha power.Next, using several algorithms (preexisting as well as novel approaches), oesophageal speech was transformed with the aim of making it more intelligible and less effortful. The novel approach consisted of a deep neural network based voice conversion system where the source was oesophageal speech and the target was synthetic speech matched in duration with the source oesophageal speech. This helped in eliminating the source-target alignment process which is particularly prone to errors for disordered speech such as oesophageal speech. Both speaker dependent and speaker independent versions of this system were implemented. The outputs of the speaker dependent system had better short term objective intelligibility scores, automatic speech recognition performance and listener preference scores compared to unprocessed oesophageal speech. The speaker independent system had improvement in short term objective intelligibility scores but not in automatic speech recognition performance. Some other signal transformations were also performed to enhance oesophageal speech. These included removal of undesired artefacts and methods to improve fundamental frequency. Out of these methods, only removal of undesired silences had success to some degree (1.44 \% points improvement in automatic speech recognition performance), and that too only for low intelligibility oesophageal speech.Lastly, the output of these transformations were evaluated and compared with previous systems using an ensemble of evaluation metrics such as short term objective intelligibility, automatic speech recognition, subjective listening tests and neural measures obtained using electroencephalography. Results reveal that the proposed neural network based system outperformed previous systems in improving the objective intelligibility and automatic speech recognition performance of oesophageal speech. In the case of subjective evaluations, the results were mixed - some positive improvement in preference scores and no improvement in speech intelligibility and listening effort scores. Overall, the results demonstrate several possibilities and new paths to enrich oesophageal speech using modern machine learning algorithms. The outcomes would be beneficial to the disordered speech community
Incorporating Ultrasound Tongue Images for Audio-Visual Speech Enhancement
Audio-visual speech enhancement (AV-SE) aims to enhance degraded speech along
with extra visual information such as lip videos, and has been shown to be more
effective than audio-only speech enhancement. This paper proposes the
incorporation of ultrasound tongue images to improve the performance of
lip-based AV-SE systems further. To address the challenge of acquiring
ultrasound tongue images during inference, we first propose to employ knowledge
distillation during training to investigate the feasibility of leveraging
tongue-related information without directly inputting ultrasound tongue images.
Specifically, we guide an audio-lip speech enhancement student model to learn
from a pre-trained audio-lip-tongue speech enhancement teacher model, thus
transferring tongue-related knowledge. To better model the alignment between
the lip and tongue modalities, we further propose the introduction of a
lip-tongue key-value memory network into the AV-SE model. This network enables
the retrieval of tongue features based on readily available lip features,
thereby assisting the subsequent speech enhancement task. Experimental results
demonstrate that both methods significantly improve the quality and
intelligibility of the enhanced speech compared to traditional lip-based AV-SE
baselines. Moreover, both proposed methods exhibit strong generalization
performance on unseen speakers and in the presence of unseen noises.
Furthermore, phone error rate (PER) analysis of automatic speech recognition
(ASR) reveals that while all phonemes benefit from introducing ultrasound
tongue images, palatal and velar consonants benefit most.Comment: Submmited to IEEE/ACM Transactions on Audio, Speech and Language
Processing. arXiv admin note: text overlap with arXiv:2305.1493
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