https://www.neurones.espci.fr/Articles_PS/SPEECHCOM%201.pdf

Silent speech interfaces

https://research.aalto.fi/files/11701996/1_s2.0_S174680941500021X_main.pdf

Current state of digital signal processing in myoelectric interfaces and related applications

Brain-computer interfaces for speech communication

Word lattice decoding has proven useful in spoken language translation; we argue that it provides a compelling model for translation of text genres, as well. We show that prior work in translating lattices using finite state techniques can be naturally extended to more expressive synchronous context-free grammarbased models. Additionally, we resolve a significant complication that non-linear word lattice inputs introduce in reordering models. Our experiments evaluating the approach demonstrate substantial gains for ChineseEnglish and Arabic-English translation.

/pdf/generalizing-word-lattice-translation-2ulznzw213.pdf

Generalizing Word Lattice Translation

In this paper, we present statistical approaches to enhance body-conducted unvoiced speech for silent speech communication. A body-conductive microphone called nonaudible murmur (NAM) microphone is effectively used to detect very soft unvoiced speech such as NAM or a whispered voice while keeping speech sounds emitted outside almost inaudible. However, body-conducted unvoiced speech is difficult to use in human-to-human speech communication because it sounds unnatural and less intelligible owing to the acoustic change caused by body conduction. To address this issue, voice conversion (VC) methods from NAM to normal speech (NAM-to-Speech) and to a whispered voice (NAM-to-Whisper) are proposed, where the acoustic features of body-conducted unvoiced speech are converted into those of natural voices in a probabilistic manner using Gaussian mixture models (GMMs). Moreover, these methods are extended to convert not only NAM but also a body-conducted whispered voice (BCW) as another type of body-conducted unvoiced speech. Several experimental evaluations are conducted to demonstrate the effectiveness of the proposed methods. The experimental results show that 1) NAM-to-Speech effectively improves intelligibility but it causes degradation of naturalness owing to the difficulty of estimating natural fundamental frequency contours from unvoiced speech; 2) NAM-to-Whisper significantly outperforms NAM-to-Speech in terms of both intelligibility and naturalness; and 3) a single conversion model capable of converting both NAM and BCW is effectively developed in our proposed VC methods.

Statistical Voice Conversion Techniques for Body-Conducted Unvoiced Speech Enhancement

We present our research on continuous speech recognition of the surface electromyographic signals that are generated by the human articulatory muscles. Previous research on electromyographic speech recognition was limited to isolated word recognition because it was very difficult to train phoneme-based acoustic models for the electromyographic speech recognizer. In this paper, we demonstrate how to train the phoneme-based acoustic models with carefully designed electromyographic feature extraction methods. By decomposing the signal into different feature space, we successfully keep the useful information while reducing the noise. Additionally, we also model the anticipatory effect of the electromyographic signals compared to the speech signal. With a 108-word decoding vocabulary, the experimental results show that the word error rate improves from 86.8% to 32.0% by using our novel feature extraction methods. Index Terms: speech recognition, electromyography, articulatory muscles, feature extraction.

http://languagelog.ldc.upenn.edu/myl/ldc/llog.backup/EMG1.pdf

Towards Continuous Speech Recognition Using Surface Electromyography

In this paper we present first experiments towards a tighter coupling between Automatic Speech Recognition (ASR) and Statistical Machine Translation (SMT) to improve the overall performance of our speech translation system. In coventional speech translation systems, the recognizer outputs a single hypothesis which is then translated by the SMT system. This approach has the limitation of being largely dependent on the word error rate of the first best hypothesis. The word error rate is typically lowered by generating many alternative hypotheses in the form of a word lattice. The information in the word lattice and the scores from the recognizer can be used by the translation system to obtain better performance. In our experiments, by switching from the single best hypotheses to word lattices as the interface between ASR and SMT, and by introducing weighted acoustic scores in the translation system, the overall performance was increased by 16.22%.

/pdf/using-word-latice-information-for-a-tighter-coupling-in-3m9ocyblu1.pdf

Using word latice information for a tighter coupling in speech translation systems.

This paper describes various adaptation methods applied to recognizing soft whisper recorded with a throat microphone. Since the amount of adaptation data is small and the testing data is very different from the training data, a series of adaptation methods is necessary. The adaptation methods include: maximum likelihood linear regression, feature-space adaptation, and re-training with downsampling, sigmoidal low-pass filter, or linear multivariate regression. With these adaptation methods, the word error rate improves from 99.3% to 32.9%.

/pdf/adaptation-for-soft-whisper-recognition-using-a-throat-1p8tci7btv.pdf

Adaptation for Soft Whisper Recognition Using a Throat Microphone

This paper describes a study of automatically identifying whispering speakers. People usually whisper in order to avoid being identified or overheard by lowering their voices. The study compares performances between normal and whispered speech mode in clean and noisy environment under matched and mismatched training conditions, and describes the impact of feature warping and throat microphone on noise reduction. Score combination strategies are used when only little whisper data is available to improve performance. In sum, we achieved 8% to 33% relative improvements in identification accuracy with only 5 to 10 seconds noisy whispered speech data per speaker.

/pdf/whispering-speaker-identification-4jxwgzq3ey.pdf

Whispering Speaker Identification

In our previous work, we reported a surface electromyographic (EMG) continuous speech recognition system with a novel EMG feature extraction method, E4, which is more robust to EMG noise than traditional spectral features. In this paper, we show that articulatory feature (AF) classifiers can also benefit from the E4 feature, which improve the F-score of the AF classifiers from 0.492 to 0.686. We also show that the E4 feature is less correlated across EMG channels and thus channel combination gains larger improvement in F-score. With a stream architecture, the AF classifiers are then integrated into the decoding framework and improve the word error rate by 11.8% relative from 33.9% to 29.9%.

/pdf/continuous-electromyographic-speech-recognition-with-a-multi-3bcps9q2vp.pdf

Szu-Chen Stan Jou

Papers

Towards Continuous Speech Recognition Using Surface Electromyography

Using word latice information for a tighter coupling in speech translation systems.

Adaptation for Soft Whisper Recognition Using a Throat Microphone

Whispering Speaker Identification

Continuous Electromyographic Speech Recognition with a Multi-Stream Decoding Architecture