DIFFERENCES BETWEEN DYSLEXIC STUDENTS AND CONTROLS MATCHED FOR EDUCATIONAL LEVEL IN WORD INTELLIGIBILITY AND TEXT COMPREHENSION PRESENTED VIA SYNTHETIC AND NATURAL SPEECH

This study investigated intelligibility and text comprehension for natural and synthetic speech held by a group of dyslexic students and their controls matched for educational level-school grade. Results have shown that both groups identified words and sentences better in natural speech. Dyslexic students however had shown worst performance in synthetic speech than controls. Overall, a significant difference has been observed between the two groups concerning their text comprehension in natural versus synthetic speech.  Article visualizations

Computer-generated speech training versus natural speech training at various task difficulty levels

Performance degradation -- Training from natural vs. automated voice

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

Deep Neural Networks for Automatic Speech-To-Speech Translation of Open Educational Resources

[ES] En los últimos años, el aprendizaje profundo ha cambiado significativamente el panorama en diversas áreas del campo de la inteligencia artificial, entre las que se incluyen la visión por computador, el procesamiento del lenguaje natural, robótica o teoría de juegos. En particular, el sorprendente éxito del aprendizaje profundo en múltiples aplicaciones del campo del procesamiento del lenguaje natural tales como el reconocimiento automático del habla (ASR), la traducción automática (MT) o la síntesis de voz (TTS), ha supuesto una mejora drástica en la precisión de estos sistemas, extendiendo así su implantación a un mayor rango de aplicaciones en la vida real. En este momento, es evidente que las tecnologías de reconocimiento automático del habla y traducción automática pueden ser empleadas para producir, de forma efectiva, subtítulos multilingües de alta calidad de contenidos audiovisuales. Esto es particularmente cierto en el contexto de los vídeos educativos, donde las condiciones acústicas son normalmente favorables para los sistemas de ASR y el discurso está gramaticalmente bien formado. Sin embargo, en el caso de TTS, aunque los sistemas basados en redes neuronales han demostrado ser capaces de sintetizar voz de un realismo y calidad sin precedentes, todavía debe comprobarse si esta tecnología está lo suficientemente madura como para mejorar la accesibilidad y la participación en el aprendizaje en línea. Además, existen diversas tareas en el campo de la síntesis de voz que todavía suponen un reto, como la clonación de voz inter-lingüe, la síntesis incremental o la adaptación zero-shot a nuevos locutores. Esta tesis aborda la mejora de las prestaciones de los sistemas actuales de síntesis de voz basados en redes neuronales, así como la extensión de su aplicación en diversos escenarios, en el contexto de mejorar la accesibilidad en el aprendizaje en línea. En este sentido, este trabajo presta especial atención a la adaptación a nuevos locutores y a la clonación de voz inter-lingüe, ya que los textos a sintetizar se corresponden, en este caso, a traducciones de intervenciones originalmente en otro idioma.[CA] Durant aquests darrers anys, l'aprenentatge profund ha canviat significativament el panorama en diverses àrees del camp de la intel·ligència artificial, entre les quals s'inclouen la visió per computador, el processament del llenguatge natural, robòtica o la teoria de jocs. En particular, el sorprenent èxit de l'aprenentatge profund en múltiples aplicacions del camp del processament del llenguatge natural, com ara el reconeixement automàtic de la parla (ASR), la traducció automàtica (MT) o la síntesi de veu (TTS), ha suposat una millora dràstica en la precisió i qualitat d'aquests sistemes, estenent així la seva implantació a un ventall més ampli a la vida real. En aquest moment, és evident que les tecnologies de reconeixement automàtic de la parla i traducció automàtica poden ser emprades per a produir, de forma efectiva, subtítols multilingües d'alta qualitat de continguts audiovisuals. Això és particularment cert en el context dels vídeos educatius, on les condicions acústiques són normalment favorables per als sistemes d'ASR i el discurs està gramaticalment ben format. No obstant això, al cas de TTS, encara que els sistemes basats en xarxes neuronals han demostrat ser capaços de sintetitzar veu d'un realisme i qualitat sense precedents, encara s'ha de comprovar si aquesta tecnologia és ja prou madura com per millorar l'accessibilitat i la participació en l'aprenentatge en línia. A més, hi ha diverses tasques al camp de la síntesi de veu que encara suposen un repte, com ara la clonació de veu inter-lingüe, la síntesi incremental o l'adaptació zero-shot a nous locutors. Aquesta tesi aborda la millora de les prestacions dels sistemes actuals de síntesi de veu basats en xarxes neuronals, així com l'extensió de la seva aplicació en diversos escenaris, en el context de millorar l'accessibilitat en l'aprenentatge en línia. En aquest sentit, aquest treball presta especial atenció a l'adaptació a nous locutors i a la clonació de veu interlingüe, ja que els textos a sintetitzar es corresponen, en aquest cas, a traduccions d'intervencions originalment en un altre idioma.[EN] In recent years, deep learning has fundamentally changed the landscapes of a number of areas in artificial intelligence, including computer vision, natural language processing, robotics, and game theory. In particular, the striking success of deep learning in a large variety of natural language processing (NLP) applications, including automatic speech recognition (ASR), machine translation (MT), and text-to-speech (TTS), has resulted in major accuracy improvements, thus widening the applicability of these technologies in real-life settings. At this point, it is clear that ASR and MT technologies can be utilized to produce cost-effective, high-quality multilingual subtitles of video contents of different kinds. This is particularly true in the case of transcription and translation of video lectures and other kinds of educational materials, in which the audio recording conditions are usually favorable for the ASR task, and there is a grammatically well-formed speech. However, although state-of-the-art neural approaches to TTS have shown to drastically improve the naturalness and quality of synthetic speech over conventional concatenative and parametric systems, it is still unclear whether this technology is already mature enough to improve accessibility and engagement in online learning, and particularly in the context of higher education. Furthermore, advanced topics in TTS such as cross-lingual voice cloning, incremental TTS or zero-shot speaker adaptation remain an open challenge in the field. This thesis is about enhancing the performance and widening the applicability of modern neural TTS technologies in real-life settings, both in offline and streaming conditions, in the context of improving accessibility and engagement in online learning. Thus, particular emphasis is placed on speaker adaptation and cross-lingual voice cloning, as the input text corresponds to a translated utterance in this context.Pérez González De Martos, AM. (2022). Deep Neural Networks for Automatic Speech-To-Speech Translation of Open Educational Resources [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/184019TESISPremios Extraordinarios de tesis doctorale

Human factors issues associated with the use of speech technology in the cockpit

The human factors issues associated with the use of voice technology in the cockpit are summarized. The formulation of the LHX avionics suite is described and the allocation of tasks to voice in the cockpit is discussed. State-of-the-art speech recognition technology is reviewed. Finally, a questionnaire designed to tap pilot opinions concerning the allocation of tasks to voice input and output in the cockpit is presented. This questionnaire was designed to be administered to operational AH-1G Cobra gunship pilots. Half of the questionnaire deals specifically with the AH-1G cockpit and the types of tasks pilots would like to have performed by voice in this existing rotorcraft. The remaining portion of the questionnaire deals with an undefined rotorcraft of the future and is aimed at determining what types of tasks these pilots would like to have performed by voice technology if anything was possible, i.e. if there were no technological constraints

Improving Dysarthric Speech Recognition by Enriching Training Datasets

Dysarthria is a motor speech disorder that results from disruptions in the neuro-motor interface and is characterised by poor articulation of phonemes and hyper-nasality and is characteristically different from normal speech. Many modern automatic speech recognition systems focus on a narrow range of speech diversity therefore as a consequence of this they exclude a groups of speakers who deviate in aspects of gender, race, age and speech impairment when building training datasets. This study attempts to develop an automatic speech recognition system that deals with dysarthric speech with limited dysarthric speech data. Speech utterances collected from the TORGO database are used to conduct experiments on a wav2vec2.0 model only trained on the Librispeech 960h dataset to obtain a baseline performance of the word error rate (WER) when recognising dysarthric speech. A version of the Librispeech model fine-tuned on multi-language datasets was tested to see if it would improve accuracy and achieved a top reduction of 24.15% in the WER for one of the male dysarthric speakers in the dataset. Transfer learning with speech recognition models and preprocessing dysarthric speech to improve its intelligibility by using general adversarial networks were limited in their potential due to a lack of dysarthric speech dataset of adequate size to use these technologies. The main conclusion drawn from this study is that a large diverse dysarthric speech dataset comparable to the size of datasets used to train machine learning ASR systems like Librispeech,with different types of speech, scripted and unscripted, is required to improve performance.

Coding Strategies for Cochlear Implants Under Adverse Environments

Cochlear implants are electronic prosthetic devices that restores partial hearing in patients with severe to profound hearing loss. Although most coding strategies have significantly improved the perception of speech in quite listening conditions, there remains limitations on speech perception under adverse environments such as in background noise, reverberation and band-limited channels, and we propose strategies that improve the intelligibility of speech transmitted over the telephone networks, reverberated speech and speech in the presence of background noise. For telephone processed speech, we propose to examine the effects of adding low-frequency and high- frequency information to the band-limited telephone speech. Four listening conditions were designed to simulate the receiving frequency characteristics of telephone handsets. Results indicated improvement in cochlear implant and bimodal listening when telephone speech was augmented with high frequency information and therefore this study provides support for design of algorithms to extend the bandwidth towards higher frequencies. The results also indicated added benefit from hearing aids for bimodal listeners in all four types of listening conditions. Speech understanding in acoustically reverberant environments is always a difficult task for hearing impaired listeners. Reverberated sounds consists of direct sound, early reflections and late reflections. Late reflections are known to be detrimental to speech intelligibility. In this study, we propose a reverberation suppression strategy based on spectral subtraction to suppress the reverberant energies from late reflections. Results from listening tests for two reverberant conditions (RT60 = 0.3s and 1.0s) indicated significant improvement when stimuli was processed with SS strategy. The proposed strategy operates with little to no prior information on the signal and the room characteristics and therefore, can potentially be implemented in real-time CI speech processors. For speech in background noise, we propose a mechanism underlying the contribution of harmonics to the benefit of electroacoustic stimulations in cochlear implants. The proposed strategy is based on harmonic modeling and uses synthesis driven approach to synthesize the harmonics in voiced segments of speech. Based on objective measures, results indicated improvement in speech quality. This study warrants further work into development of algorithms to regenerate harmonics of voiced segments in the presence of noise