How speaker tongue and name source language affect the automatic recognition of spoken names

In this paper the automatic recognition of person names and geographical names uttered by native and non-native speakers is examined in an experimental set-up. The major aim was to raise our understanding of how well and under which circumstances previously proposed methods of multilingual pronunciation modeling and multilingual acoustic modeling contribute to a better name recognition in a cross-lingual context. To come to a meaningful interpretation of results we have categorized each language according to the amount of exposure a native speaker is expected to have had to this language. After having interpreted our results we have also tried to find an answer to the question of how much further improvement one might be able to attain with a more advanced pronunciation modeling technique which we plan to develop

Essential Speech and Language Technology for Dutch: Results by the STEVIN-programme

Computational Linguistics; Germanic Languages; Artificial Intelligence (incl. Robotics); Computing Methodologie

Vowel recognition in continuous speech

In continuous speech, the identification of phonemes requires the ability to extract features that are capable of characterizing the acoustic signal. Previous work has shown that relatively high classification accuracy can be obtained from a single spectrum taken during the steady-state portion of the phoneme, assuming that the phonetic environment is held constant. The present study represents an attempt to extend this work to variable phonetic contexts by using dynamic rather than static spectral information. This thesis has four aims: 1) Classify vowels in continuous speech; 2) Find the optimal set of features that best describe the vowel regions; 3) Compare the classification results using a multivariate maximum likelihood distance measure with those of a neural network using the backpropagation model; 4) Examine the classification performance of a Hidden Markov Model given a pathway through phonetic space

Individual Differences in Speech Production and Perception

Inter-individual variation in speech is a topic of increasing interest both in human sciences and speech technology. It can yield important insights into biological, cognitive, communicative, and social aspects of language. Written by specialists in psycholinguistics, phonetics, speech development, speech perception and speech technology, this volume presents experimental and modeling studies that provide the reader with a deep understanding of interspeaker variability and its role in speech processing, speech development, and interspeaker interactions. It discusses how theoretical models take into account individual behavior, explains why interspeaker variability enriches speech communication, and summarizes the limitations of the use of speaker information in forensics

Error analysis in automatic speech recognition and machine translation

Automatic speech recognition and machine translation are well-known terms in the translation world nowadays. Systems that carry out these processes are taking over the work of humans more and more. Reasons for this are the speed at which the tasks are performed and their costs. However, the quality of these systems is debatable. They are not yet capable of delivering the same performance as human transcribers or translators. The lack of creativity, the ability to interpret texts and the sense of language is often cited as the reason why the performance of machines is not yet at the level of human translation or transcribing work. Despite this, there are companies that use these machines in their production pipelines. Unbabel, an online translation platform powered by artificial intelligence, is one of these companies. Through a combination of human translators and machines, Unbabel tries to provide its customers with a translation of good quality. This internship report was written with the aim of gaining an overview of the performance of these systems and the errors they produce. Based on this work, we try to get a picture of possible error patterns produced by both systems. The present work consists of an extensive analysis of errors produced by automatic speech recognition and machine translation systems after automatically transcribing and translating 10 English videos into Dutch. Different videos were deliberately chosen to see if there were significant differences in the error patterns between videos. The generated data and results from this work, aims at providing possible ways to improve the quality of the services already mentioned.O reconhecimento automático de fala e a tradução automática são termos conhecidos no mundo da tradução, hoje em dia. Os sistemas que realizam esses processos estão a assumir cada vez mais o trabalho dos humanos. As razões para isso são a velocidade com que as tarefas são realizadas e os seus custos. No entanto, a qualidade desses sistemas é discutível. As máquinas ainda não são capazes de ter o mesmo desempenho dos transcritores ou tradutores humanos. A falta de criatividade, de capacidade de interpretar textos e de sensibilidade linguística são motivos frequentemente usados para justificar o facto de as máquinas ainda não estarem suficientemente desenvolvidas para terem um desempenho comparável com o trabalho de tradução ou transcrição humano. Mesmo assim, existem empresas que fazem uso dessas máquinas. A Unbabel, uma plataforma de tradução online baseada em inteligência artificial, é uma dessas empresas. Através de uma combinação de tradutores humanos e de máquinas, a Unbabel procura oferecer aos seus clientes traduções de boa qualidade. O presente relatório de estágio foi feito com o intuito de obter uma visão geral do desempenho desses sistemas e das falhas que cometem, propondo delinear uma imagem dos possíveis padrões de erro existentes nos mesmos. Para tal, fez-se uma análise extensa das falhas que os sistemas de reconhecimento automático de fala e de tradução automática cometeram, após a transcrição e a tradução automática de 10 vídeos. Foram deliberadamente escolhidos registos videográficos diversos, de modo a verificar possíveis diferenças nos padrões de erro. Através dos dados gerados e dos resultados obtidos, propõe-se encontrar uma forma de melhorar a qualidade dos serviços já mencionados

NeuroSpeech

NeuroSpeech is a software for modeling pathological speech signals considering different speech dimensions: phonation, articulation, prosody, and intelligibility. Although it was developed to model dysarthric speech signals from Parkinson's patients, its structure allows other computer scientists or developers to include other pathologies and/or measures. Different tasks can be performed: (1) modeling of the signals considering the aforementioned speech dimensions, (2) automatic discrimination of Parkinson's vs. non-Parkinson's, and (3) prediction of the neurological state according to the Unified Parkinson's Disease Rating Scale (UPDRS) score. The prediction of the dysarthria level according to the Frenchay Dysarthria Assessment scale is also provided

Paralinguistic vocal control of interactive media: how untapped elements of voice might enhance the role of non-speech voice input in the user's experience of multimedia.

Much interactive media development, especially commercial development, implies the dominance of the visual modality, with sound as a limited supporting channel. The development of multimedia technologies such as augmented reality and virtual reality has further revealed a distinct partiality to visual media. Sound, however, and particularly voice, have many aspects which have yet to be adequately investigated. Exploration of these aspects may show that sound can, in some respects, be superior to graphics in creating immersive and expressive interactive experiences. With this in mind, this thesis investigates the use of non-speech voice characteristics as a complementary input mechanism in controlling multimedia applications. It presents a number of projects that employ the paralinguistic elements of voice as input to interactive media including both screen-based and physical systems. These projects are used as a means of exploring the factors that seem likely to affect users’ preferences and interaction patterns during non-speech voice control. This exploration forms the basis for an examination of potential roles for paralinguistic voice input. The research includes the conceptual and practical development of the projects and a set of evaluative studies. The work submitted for Ph.D. comprises practical projects (50 percent) and a written dissertation (50 percent). The thesis aims to advance understanding of how voice can be used both on its own and in combination with other input mechanisms in controlling multimedia applications. It offers a step forward in the attempts to integrate the paralinguistic components of voice as a complementary input mode to speech input applications in order to create a synergistic combination that might let the strengths of each mode overcome the weaknesses of the other

Towards an Integrative Information Society: Studies on Individuality in Speech and Sign

The flow of information within modern information society has increased rapidly over the last decade. The major part of this information flow relies on the individual’s abilities to handle text or speech input. For the majority of us it presents no problems, but there are some individuals who would benefit from other means of conveying information, e.g. signed information flow. During the last decades the new results from various disciplines have all suggested towards the common background and processing for sign and speech and this was one of the key issues that I wanted to investigate further in this thesis. The basis of this thesis is firmly within speech research and that is why I wanted to design analogous test batteries for widely used speech perception tests for signers – to find out whether the results for signers would be the same as in speakers’ perception tests. One of the key findings within biology – and more precisely its effects on speech and communication research – is the mirror neuron system. That finding has enabled us to form new theories about evolution of communication, and it all seems to converge on the hypothesis that all communication has a common core within humans. In this thesis speech and sign are discussed as equal and analogical counterparts of communication and all research methods used in speech are modified for sign. Both speech and sign are thus investigated using similar test batteries. Furthermore, both production and perception of speech and sign are studied separately. An additional framework for studying production is given by gesture research using cry sounds. Results of cry sound research are then compared to results from children acquiring sign language. These results show that individuality manifests itself from very early on in human development. Articulation in adults, both in speech and sign, is studied from two perspectives: normal production and re-learning production when the apparatus has been changed. Normal production is studied both in speech and sign and the effects of changed articulation are studied with regards to speech. Both these studies are done by using carrier sentences. Furthermore, sign production is studied giving the informants possibility for spontaneous speech. The production data from the signing informants is also used as the basis for input in the sign synthesis stimuli used in sign perception test battery. Speech and sign perception were studied using the informants’ answers to questions using forced choice in identification and discrimination tasks. These answers were then compared across language modalities. Three different informant groups participated in the sign perception tests: native signers, sign language interpreters and Finnish adults with no knowledge of any signed language. This gave a chance to investigate which of the characteristics found in the results were due to the language per se and which were due to the changes in modality itself. As the analogous test batteries yielded similar results over different informant groups, some common threads of results could be observed. Starting from very early on in acquiring speech and sign the results were highly individual. However, the results were the same within one individual when the same test was repeated. This individuality of results represented along same patterns across different language modalities and - in some occasions - across language groups. As both modalities yield similar answers to analogous study questions, this has lead us to providing methods for basic input for sign language applications, i.e. signing avatars. This has also given us answers to questions on precision of the animation and intelligibility for the users – what are the parameters that govern intelligibility of synthesised speech or sign and how precise must the animation or synthetic speech be in order for it to be intelligible. The results also give additional support to the well-known fact that intelligibility in fact is not the same as naturalness. In some cases, as shown within the sign perception test battery design, naturalness decreases intelligibility. This also has to be taken into consideration when designing applications. All in all, results from each of the test batteries, be they for signers or speakers, yield strikingly similar patterns, which would indicate yet further support for the common core for all human communication. Thus, we can modify and deepen the phonetic framework models for human communication based on the knowledge obtained from the results of the test batteries within this thesis.Siirretty Doriast