120 research outputs found
Multi-Modal Automatic Prosody Annotation with Contrastive Pretraining of SSWP
In the realm of expressive Text-to-Speech (TTS), explicit prosodic boundaries
significantly advance the naturalness and controllability of synthesized
speech. While human prosody annotation contributes a lot to the performance, it
is a labor-intensive and time-consuming process, often resulting in
inconsistent outcomes. Despite the availability of extensive supervised data,
the current benchmark model still faces performance setbacks. To address this
issue, a two-stage automatic annotation pipeline is novelly proposed in this
paper. Specifically, in the first stage, we propose contrastive text-speech
pretraining of Speech-Silence and Word-Punctuation (SSWP) pairs. The
pretraining procedure hammers at enhancing the prosodic space extracted from
joint text-speech space. In the second stage, we build a multi-modal prosody
annotator, which consists of pretrained encoders, a straightforward yet
effective text-speech feature fusion scheme, and a sequence classifier.
Extensive experiments conclusively demonstrate that our proposed method excels
at automatically generating prosody annotation and achieves state-of-the-art
(SOTA) performance. Furthermore, our novel model has exhibited remarkable
resilience when tested with varying amounts of data.Comment: Submitted to ICASSP 202
Cross-Utterance Conditioned VAE for Speech Generation
Speech synthesis systems powered by neural networks hold promise for
multimedia production, but frequently face issues with producing expressive
speech and seamless editing. In response, we present the Cross-Utterance
Conditioned Variational Autoencoder speech synthesis (CUC-VAE S2) framework to
enhance prosody and ensure natural speech generation. This framework leverages
the powerful representational capabilities of pre-trained language models and
the re-expression abilities of variational autoencoders (VAEs). The core
component of the CUC-VAE S2 framework is the cross-utterance CVAE, which
extracts acoustic, speaker, and textual features from surrounding sentences to
generate context-sensitive prosodic features, more accurately emulating human
prosody generation. We further propose two practical algorithms tailored for
distinct speech synthesis applications: CUC-VAE TTS for text-to-speech and
CUC-VAE SE for speech editing. The CUC-VAE TTS is a direct application of the
framework, designed to generate audio with contextual prosody derived from
surrounding texts. On the other hand, the CUC-VAE SE algorithm leverages real
mel spectrogram sampling conditioned on contextual information, producing audio
that closely mirrors real sound and thereby facilitating flexible speech
editing based on text such as deletion, insertion, and replacement.
Experimental results on the LibriTTS datasets demonstrate that our proposed
models significantly enhance speech synthesis and editing, producing more
natural and expressive speech.Comment: 13 pages
SYNTHESIZING DYSARTHRIC SPEECH USING MULTI-SPEAKER TTS FOR DSYARTHRIC SPEECH RECOGNITION
Dysarthria is a motor speech disorder often characterized by reduced speech intelligibility through slow, uncoordinated control of speech production muscles. Automatic Speech recognition (ASR) systems may help dysarthric talkers communicate more effectively. However, robust dysarthria-specific ASR requires a significant amount of training speech is required, which is not readily available for dysarthric talkers.
In this dissertation, we investigate dysarthric speech augmentation and synthesis methods. To better understand differences in prosodic and acoustic characteristics of dysarthric spontaneous speech at varying severity levels, a comparative study between typical and dysarthric speech was conducted. These characteristics are important components for dysarthric speech modeling, synthesis, and augmentation. For augmentation, prosodic transformation and time-feature masking have been proposed. For dysarthric speech synthesis, this dissertation has introduced a modified neural multi-talker TTS by adding a dysarthria severity level coefficient and a pause insertion model to synthesize dysarthric speech for varying severity levels. In addition, we have extended this work by using a label propagation technique to create more meaningful control variables such as a continuous Respiration, Laryngeal and Tongue (RLT) parameter, even for datasets that only provide discrete dysarthria severity level information. This approach increases the controllability of the system, so we are able to generate more dysarthric speech with a broader range.
To evaluate their effectiveness for synthesis of training data, dysarthria-specific speech recognition was used. Results show that a DNN-HMM model trained on additional synthetic dysarthric speech achieves WER improvement of 12.2% compared to the baseline, and that the addition of the severity level and pause insertion controls decrease WER by 6.5%, showing the effectiveness of adding these parameters. Overall results on the TORGO database demonstrate that using dysarthric synthetic speech to increase the amount of dysarthric-patterned speech for training has a significant impact on the dysarthric ASR systems
Dual-level segmentation method for feature extraction enhancement strategy in speech emotion recognition
The speech segmentation approach could be one of the significant factors contributing to a Speech Emotion Recognition (SER) system's overall performance. An utterance may contain more than one perceived emotion, the boundaries between the changes of emotion in an utterance are challenging to determine. Speech segmented through the conventional fixed window did not correspond to the signal changes, due to the random segment point, an arbitrary segmented frame is produced, the segment boundary might be within the sentence or in-between emotional changes. This study introduced an improvement of segment-based segmentation on a fixed-window Relative Time Interval (RTI) by using Signal Change (SC) segmentation approach to discover the signal boundary concerning the signal transition. A segment-based feature extraction enhancement strategy using a dual-level segmentation method was proposed: RTI-SC segmentation utilizing the conventional approach. Instead of segmenting the whole utterance at the relative time interval, this study implements peak analysis to obtain segment boundaries defined by the maximum peak value within each temporary RTI segment. In peak selection, over-segmentation might occur due to connections with the input signal, impacting the boundary selection decision. Two approaches in finding the maximum peaks were implemented, firstly; peak selection by distance allocation, and secondly; peak selection by Maximum function. The substitution of the temporary RTI segment with the segment concerning signal change was intended to capture better high-level statistical-based features within the signal transition. The signal's prosodic, spectral, and wavelet properties were integrated to structure a fine feature set based on the proposed method. 36 low-level descriptors and 12 statistical features and their derivative were extracted on each segment resulted in a fixed vector dimension. Correlation-based Feature Subset Selection (CFS) with the Best First search method was applied for dimensionality reduction before Support Vector Machine (SVM) with Sequential Minimal Optimization (SMO) was implemented for classification. The performance of the feature fusion constructed from the proposed method was evaluated through speaker-dependent and speaker-independent tests on EMO-DB and RAVDESS databases. The result indicated that the prosodic and spectral feature derived from the dual-level segmentation method offered a higher recognition rate for most speaker-independent tasks with a significant improvement of the overall accuracy of 82.2% (150 features), the highest accuracy among other segmentation approaches used in this study. The proposed method outperformed the baseline approach in a single emotion assessment in both full dimensions and an optimized set. The highest accuracy for every emotion was mostly contributed by the proposed method. Using the EMO-DB database, accuracy was enhanced, specifically, happy (67.6%), anger (89%), fear (85.5%), disgust (79.3%), while neutral and sadness emotion obtained a similar accuracy with the baseline method (91%) and (93.5%) respectively. A 100% accuracy for boredom emotion (female speaker) was observed in the speaker-dependent test, the highest single emotion classified, reported in this study
A Review of Deep Learning Techniques for Speech Processing
The field of speech processing has undergone a transformative shift with the
advent of deep learning. The use of multiple processing layers has enabled the
creation of models capable of extracting intricate features from speech data.
This development has paved the way for unparalleled advancements in speech
recognition, text-to-speech synthesis, automatic speech recognition, and
emotion recognition, propelling the performance of these tasks to unprecedented
heights. The power of deep learning techniques has opened up new avenues for
research and innovation in the field of speech processing, with far-reaching
implications for a range of industries and applications. This review paper
provides a comprehensive overview of the key deep learning models and their
applications in speech-processing tasks. We begin by tracing the evolution of
speech processing research, from early approaches, such as MFCC and HMM, to
more recent advances in deep learning architectures, such as CNNs, RNNs,
transformers, conformers, and diffusion models. We categorize the approaches
and compare their strengths and weaknesses for solving speech-processing tasks.
Furthermore, we extensively cover various speech-processing tasks, datasets,
and benchmarks used in the literature and describe how different deep-learning
networks have been utilized to tackle these tasks. Additionally, we discuss the
challenges and future directions of deep learning in speech processing,
including the need for more parameter-efficient, interpretable models and the
potential of deep learning for multimodal speech processing. By examining the
field's evolution, comparing and contrasting different approaches, and
highlighting future directions and challenges, we hope to inspire further
research in this exciting and rapidly advancing field
Effects of errorless learning on the acquisition of velopharyngeal movement control
Session 1pSC - Speech Communication: Cross-Linguistic Studies of Speech Sound Learning of the Languages of Hong Kong (Poster Session)The implicit motor learning literature suggests a benefit for learning if errors are minimized during practice. This study investigated whether the same principle holds for learning velopharyngeal movement control. Normal speaking participants learned to produce hypernasal speech in either an errorless learning condition (in which the possibility for errors was limited) or an errorful learning condition (in which the possibility for errors was not limited). Nasality level of the participants’ speech was measured by nasometer and reflected by nasalance scores (in %). Errorless learners practiced producing hypernasal speech with a threshold nasalance score of 10% at the beginning, which gradually increased to a threshold of 50% at the end. The same set of threshold targets were presented to errorful learners but in a reversed order. Errors were defined by the proportion of speech with a nasalance score below the threshold. The results showed that, relative to errorful learners, errorless learners displayed fewer errors (50.7% vs. 17.7%) and a higher mean nasalance score (31.3% vs. 46.7%) during the acquisition phase. Furthermore, errorless learners outperformed errorful learners in both retention and novel transfer tests. Acknowledgment: Supported by The University of Hong Kong Strategic Research Theme for Sciences of Learning © 2012 Acoustical Society of Americapublished_or_final_versio
IberSPEECH 2020: XI Jornadas en TecnologĂa del Habla and VII Iberian SLTech
IberSPEECH2020 is a two-day event, bringing together the best researchers and practitioners in speech and language technologies in Iberian languages to promote interaction and discussion. The organizing committee has planned a wide variety of scientific and social activities, including technical paper presentations, keynote lectures, presentation of projects, laboratories activities, recent PhD thesis, discussion panels, a round table, and awards to the best thesis and papers. The program of IberSPEECH2020 includes a total of 32 contributions that will be presented distributed among 5 oral sessions, a PhD session, and a projects session. To ensure the quality of all the contributions, each submitted paper was reviewed by three members of the scientific review committee. All the papers in the conference will be accessible through the International Speech Communication Association (ISCA) Online Archive. Paper selection was based on the scores and comments provided by the scientific review committee, which includes 73 researchers from different institutions (mainly from Spain and Portugal, but also from France, Germany, Brazil, Iran, Greece, Hungary, Czech Republic, Ucrania, Slovenia). Furthermore, it is confirmed to publish an extension of selected papers as a special issue of the Journal of Applied Sciences, “IberSPEECH 2020: Speech and Language Technologies for Iberian Languages”, published by MDPI with fully open access. In addition to regular paper sessions, the IberSPEECH2020 scientific program features the following activities: the ALBAYZIN evaluation challenge session.Red Española de TecnologĂas del Habla. Universidad de Valladoli
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