Phonotactic learning with neural language models

Computational models of phonotactics share much in common with language models, which assign probabilities to sequences of words. While state of the art language models are implemented using neural networks, phonotactic models have not followed suit. We present several neural models of phonotactics, and show that they perform favorably when compared to existing models. In addition, they provide useful insights into the role of representations on phonotactic learning and generalization. This work provides a promising starting point for future modeling of human phonotactic knowledge

Suprasegmental representations for the modeling of fundamental frequency in statistical parametric speech synthesis

Statistical parametric speech synthesis (SPSS) has seen improvements over recent years, especially in terms of intelligibility. Synthetic speech is often clear and understandable, but it can also be bland and monotonous. Proper generation of natural speech prosody is still a largely unsolved problem. This is relevant especially in the context of expressive audiobook speech synthesis, where speech is expected to be fluid and captivating. In general, prosody can be seen as a layer that is superimposed on the segmental (phone) sequence. Listeners can perceive the same melody or rhythm in different utterances, and the same segmental sequence can be uttered with a different prosodic layer to convey a different message. For this reason, prosody is commonly accepted to be inherently suprasegmental. It is governed by longer units within the utterance (e.g. syllables, words, phrases) and beyond the utterance (e.g. discourse). However, common techniques for the modeling of speech prosody - and speech in general - operate mainly on very short intervals, either at the state or frame level, in both hidden Markov model (HMM) and deep neural network (DNN) based speech synthesis. This thesis presents contributions supporting the claim that stronger representations of suprasegmental variation are essential for the natural generation of fundamental frequency for statistical parametric speech synthesis. We conceptualize the problem by dividing it into three sub-problems: (1) representations of acoustic signals, (2) representations of linguistic contexts, and (3) the mapping of one representation to another. The contributions of this thesis provide novel methods and insights relating to these three sub-problems. In terms of sub-problem 1, we propose a multi-level representation of f0 using the continuous wavelet transform and the discrete cosine transform, as well as a wavelet-based decomposition strategy that is linguistically and perceptually motivated. In terms of sub-problem 2, we investigate additional linguistic features such as text-derived word embeddings and syllable bag-of-phones and we propose a novel method for learning word vector representations based on acoustic counts. Finally, considering sub-problem 3, insights are given regarding hierarchical models such as parallel and cascaded deep neural networks

Subspace Gaussian Mixture Models for Language Identification and Dysarthric Speech Intelligibility Assessment

En esta Tesis se ha investigado la aplicación de técnicas de modelado de subespacios de mezclas de Gaussianas en dos problemas relacionados con las tecnologías del habla, como son la identificación automática de idioma (LID, por sus siglas en inglés) y la evaluación automática de inteligibilidad en el habla de personas con disartria. Una de las técnicas más importantes estudiadas es el análisis factorial conjunto (JFA, por sus siglas en inglés). JFA es, en esencia, un modelo de mezclas de Gaussianas en el que la media de cada componente se expresa como una suma de factores de dimensión reducida, y donde cada factor representa una contribución diferente a la señal de audio. Esta factorización nos permite compensar nuestros modelos frente a contribuciones indeseadas presentes en la señal, como la información de canal. JFA se ha investigado como clasficador y como extractor de parámetros. En esta última aproximación se modela un solo factor que representa todas las contribuciones presentes en la señal. Los puntos en este subespacio se denominan i-Vectors. Así, un i-Vector es un vector de baja dimensión que representa una grabación de audio. Los i-Vectors han resultado ser muy útiles como vector de características para representar señales en diferentes problemas relacionados con el aprendizaje de máquinas. En relación al problema de LID, se han investigado dos sistemas diferentes de acuerdo al tipo de información extraída de la señal. En el primero, la señal se parametriza en vectores acústicos con información espectral a corto plazo. En este caso, observamos mejoras de hasta un 50% con el sistema basado en i-Vectors respecto al sistema que utilizaba JFA como clasificador. Se comprobó que el subespacio de canal del modelo JFA también contenía información del idioma, mientras que con los i-Vectors no se descarta ningún tipo de información, y además, son útiles para mitigar diferencias entre los datos de entrenamiento y de evaluación. En la fase de clasificación, los i-Vectors de cada idioma se modelaron con una distribución Gaussiana en la que la matriz de covarianza era común para todos. Este método es simple y rápido, y no requiere de ningún post-procesado de los i-Vectors. En el segundo sistema, se introdujo el uso de información prosódica y formántica en un sistema de LID basado en i-Vectors. La precisión de éste estaba por debajo de la del sistema acústico. Sin embargo, los dos sistemas son complementarios, y se obtuvo hasta un 20% de mejora con la fusión de los dos respecto al sistema acústico solo. Tras los buenos resultados obtenidos para LID, y dado que, teóricamente, los i-Vectors capturan toda la información presente en la señal, decidimos usarlos para la evaluar de manera automática la inteligibilidad en el habla de personas con disartria. Los logopedas están muy interesados en esta tecnología porque permitiría evaluar a sus pacientes de una manera objetiva y consistente. En este caso, los i-Vectors se obtuvieron a partir de información espectral a corto plazo de la señal, y la inteligibilidad se calculó a partir de los i-Vectors obtenidos para un conjunto de palabras dichas por el locutor evaluado. Comprobamos que los resultados eran mucho mejores si en el entrenamiento del sistema se incorporaban datos de la persona que iba a ser evaluada. No obstante, esta limitación podría aliviarse utilizando una mayor cantidad de datos para entrenar el sistema.In this Thesis, we investigated how to effciently apply subspace Gaussian mixture modeling techniques onto two speech technology problems, namely automatic spoken language identification (LID) and automatic intelligibility assessment of dysarthric speech. One of the most important of such techniques in this Thesis was joint factor analysis (JFA). JFA is essentially a Gaussian mixture model where the mean of the components is expressed as a sum of low-dimension factors that represent different contributions to the speech signal. This factorization makes it possible to compensate for undesired sources of variability, like the channel. JFA was investigated as final classiffer and as feature extractor. In the latter approach, a single subspace including all sources of variability is trained, and points in this subspace are known as i-Vectors. Thus, one i-Vector is defined as a low-dimension representation of a single utterance, and they are a very powerful feature for different machine learning problems. We have investigated two different LID systems according to the type of features extracted from speech. First, we extracted acoustic features representing short-time spectral information. In this case, we observed relative improvements with i-Vectors with respect to JFA of up to 50%. We realized that the channel subspace in a JFA model also contains language information whereas i-Vectors do not discard any language information, and moreover, they help to reduce mismatches between training and testing data. For classification, we modeled the i-Vectors of each language with a Gaussian distribution with covariance matrix shared among languages. This method is simple and fast, and it worked well without any post-processing. Second, we introduced the use of prosodic and formant information with the i-Vectors system. The performance was below the acoustic system but both were found to be complementary and we obtained up to a 20% relative improvement with the fusion with respect to the acoustic system alone. Given the success in LID and the fact that i-Vectors capture all the information that is present in the data, we decided to use i-Vectors for other tasks, specifically, the assessment of speech intelligibility in speakers with different types of dysarthria. Speech therapists are very interested in this technology because it would allow them to objectively and consistently rate the intelligibility of their patients. In this case, the input features were extracted from short-term spectral information, and the intelligibility was assessed from the i-Vectors calculated from a set of words uttered by the tested speaker. We found that the performance was clearly much better if we had available data for training of the person that would use the application. We think that this limitation could be relaxed if we had larger databases for training. However, the recording process is not easy for people with disabilities, and it is difficult to obtain large datasets of dysarthric speakers open to the research community. Finally, the same system architecture for intelligibility assessment based on i-Vectors was used for predicting the accuracy that an automatic speech recognizer (ASR) system would obtain with dysarthric speakers. The only difference between both was the ground truth label set used for training. Predicting the performance response of an ASR system would increase the confidence of speech therapists in these systems and would diminish health related costs. The results were not as satisfactory as in the previous case, probably because an ASR is a complex system whose accuracy can be very difficult to be predicted only with acoustic information. Nonetheless, we think that we opened a door to an interesting research direction for the two problems

Stress recognition from speech signal

Předložená disertační práce se zabývá vývojem algoritmů pro detekci stresu z řečového signálu. Inovativnost této práce se vyznačuje dvěma typy analýzy řečového signálu, a to za použití samohláskových polygonů a analýzy hlasivkových pulsů. Obě tyto základní analýzy mohou sloužit k detekci stresu v řečovém signálu, což bylo dokázáno sérií provedených experimentů. Nejlepších výsledků bylo dosaženo pomocí tzv. Closing-To-Opening phase ratio příznaku v Top-To-Bottom kritériu v kombinaci s vhodným klasifikátorem. Detekce stresu založená na této analýze může být definována jako jazykově i fonémově nezávislá, což bylo rovněž dokázáno získanými výsledky, které dosahují v některých případech až 95% úspěšnosti. Všechny experimenty byly provedeny na vytvořené české databázi obsahující reálný stres, a některé experimenty byly také provedeny pro anglickou stresovou databázi SUSAS.Presented doctoral thesis is focused on development of algorithms for psychological stress detection in speech signal. The novelty of this thesis aims on two different analysis of the speech signal- the analysis of vowel polygons and the analysis of glottal pulses. By performed experiments, the doctoral thesis uncovers the possible usage of both fundamental analyses for psychological stress detection in speech. The analysis of glottal pulses in amplitude domain according to Top-To-Bottom criterion seems to be as the most effective with the combination of properly chosen classifier, which can be defined as language and phoneme independent way to stress recognition. All experiments were performed on developed Czech real stress database and some observations were also made on English database SUSAS. The variety of possibly effective ways of stress recognition in speech leads to approach very high recognition accuracy of their combination, or of their possible usage for detection of other speaker’s state, which has to be further tested and verified by appropriate databases.

The Processing of Accented Speech

This thesis examines the processing of accented speech in both infants and adults. Accents provide a natural and reasonably consistent form of inter-speaker variation in the speech signal, but it is not yet clear exactly what processes are used to normalise this form of variation, or when and how those processes develop. Two adult studies use ERP data to examine differences between the online processing of regional- and foreign-accented speech as compared to a baseline consisting of the listeners’ home accent. These studies demonstrate that the two types of accents recruit normalisation processes which are qualitatively, and not just quantitatively, different. This provided support for the hypothesis that foreign and regional accents require different mechanisms to normalise accent-based variation (Adank et al., 2009, Floccia et al., 2009), rather than for the hypothesis that different types of accents are normalised according to their perceptual distance from the listener’s own accent (Clarke & Garrett, 2004). They also provide support for the Abstract entry approach to lexical storage of variant forms, which suggests that variant forms undergo a process of prelexical normalisation, allowing access to a canonical lexical entry (Pallier et al., 2001), rather than for the Exemplar-based approach, which suggests that variant word-forms are individually represented in the lexicon (Johnson, 1997). Two further studies examined how infants segment words from continuous speech when presented with accented speakers. The first of these includes a set of behavioural experiments, which highlight some methodological issues in the existing literature and offer some potential explanations for conflicting evidence about the age at which infants are able to segment speech. The second uses ERP data to investigate segmentation within and across accents, and provides neurophysiological evidence that 11-month-olds are able to distinguish newly-segmented words at the auditory level even within a foreign accent, or across accents, but that they are more able to treat new word-forms as word-like in a familiar accent than a foreign accent

The Nature of Writing – A Theory of Grapholinguistics [book cover]

Cover illustration: Purgatory: Canto VII – The Rule of the Mountain from A Typographic Dante (2008) by Barrie Tullett (also displayed in Barrie Tullett, Typewriter Art: A Modern Anthology, London: Laurence King Publishing, 2014, p. 167). With kind permission by Barrie Tullett. The text is taken from Dante. The Divine Comedy, translated by Dorothy L. Sayers, Harmondsworth­Middlesex: The Penguin Classics, 1949. On the lower part of the illustration, one can read the concluding verses of the Canto: But now the poet was going on before; “Forward!” said he; “look how the sun doth stand Meridian­high, while on the Western shore Night sets her foot upon Morocco’s strand.