Development of the Slovak HMM-Based TTS System and Evaluation of Voices in Respect to the Used Vocoding Techniques

This paper describes the development of a Slovak text-to-speech system which applies a technique wherein speech is directly synthesized from hidden Markov models. Statistical models for Slovak speech units are trained by using the newly created female and male phonetically balanced speech corpora. In addition, contextual informations about phonemes, syllables, words, phrases, and utterances were determined, as well as questions for decision tree-based context clustering algorithms. In this paper, recent statistical parametric speech synthesis methods including the conventional, STRAIGHT and AHOcoder speech synthesis systems are implemented and evaluated. Objective evaluation methods (mel-cepstral distortion and fundamental frequency comparison) and subjective ones (mean opinion score and semantically unpredictable sentences test) are carried out to compare these systems with each other and evaluation of their overall quality. The result of this work is a set of text to speech systems for Slovak language which are characterized by very good intelligibility and quite good naturalness of utterances at the output of these systems. In the subjective tests of intelligibility the STRAIGHT based female voice and AHOcoder based male voice reached the highest scores

Reducing Audible Spectral Discontinuities

In this paper, a common problem in diphone synthesis is discussed, viz., the occurrence of audible discontinuities at diphone boundaries. Informal observations show that spectral mismatch is most likely the cause of this phenomenon.We first set out to find an objective spectral measure for discontinuity. To this end, several spectral distance measures are related to the results of a listening experiment. Then, we studied the feasibility of extending the diphone database with context-sensitive diphones to reduce the occurrence of audible discontinuities. The number of additional diphones is limited by clustering consonant contexts that have a similar effect on the surrounding vowels on the basis of the best performing distance measure. A listening experiment has shown that the addition of these context-sensitive diphones significantly reduces the amount of audible discontinuities

Automatic prosodic analysis for computer aided pronunciation teaching

Correct pronunciation of spoken language requires the appropriate modulation of acoustic characteristics of speech to convey linguistic information at a suprasegmental level. Such prosodic modulation is a key aspect of spoken language and is an important component of foreign language learning, for purposes of both comprehension and intelligibility. Computer aided pronunciation teaching involves automatic analysis of the speech of a non-native talker in order to provide a diagnosis of the learner's performance in comparison with the speech of a native talker. This thesis describes research undertaken to automatically analyse the prosodic aspects of speech for computer aided pronunciation teaching. It is necessary to describe the suprasegmental composition of a learner's speech in order to characterise significant deviations from a native-like prosody, and to offer some kind of corrective diagnosis. Phonological theories of prosody aim to describe the suprasegmental composition of speech..

An evaluation of intrusive instrumental intelligibility metrics

Instrumental intelligibility metrics are commonly used as an alternative to listening tests. This paper evaluates 12 monaural intrusive intelligibility metrics: SII, HEGP, CSII, HASPI, NCM, QSTI, STOI, ESTOI, MIKNN, SIMI, SIIB, and $\text{sEPSM}^\text{corr}$. In addition, this paper investigates the ability of intelligibility metrics to generalize to new types of distortions and analyzes why the top performing metrics have high performance. The intelligibility data were obtained from 11 listening tests described in the literature. The stimuli included Dutch, Danish, and English speech that was distorted by additive noise, reverberation, competing talkers, pre-processing enhancement, and post-processing enhancement. SIIB and HASPI had the highest performance achieving a correlation with listening test scores on average of $\rho=0.92$ and $\rho=0.89$, respectively. The high performance of SIIB may, in part, be the result of SIIBs developers having access to all the intelligibility data considered in the evaluation. The results show that intelligibility metrics tend to perform poorly on data sets that were not used during their development. By modifying the original implementations of SIIB and STOI, the advantage of reducing statistical dependencies between input features is demonstrated. Additionally, the paper presents a new version of SIIB called $\text{SIIB}^\text{Gauss}$, which has similar performance to SIIB and HASPI, but takes less time to compute by two orders of magnitude.Comment: Published in IEEE/ACM Transactions on Audio, Speech, and Language Processing, 201

The development of the audio-visual voice comparator for speech and hearing therapy

Thesis (Ed.M.)--Boston Universit

Deep Learning for Audio Signal Processing

Given the recent surge in developments of deep learning, this article provides a review of the state-of-the-art deep learning techniques for audio signal processing. Speech, music, and environmental sound processing are considered side-by-side, in order to point out similarities and differences between the domains, highlighting general methods, problems, key references, and potential for cross-fertilization between areas. The dominant feature representations (in particular, log-mel spectra and raw waveform) and deep learning models are reviewed, including convolutional neural networks, variants of the long short-term memory architecture, as well as more audio-specific neural network models. Subsequently, prominent deep learning application areas are covered, i.e. audio recognition (automatic speech recognition, music information retrieval, environmental sound detection, localization and tracking) and synthesis and transformation (source separation, audio enhancement, generative models for speech, sound, and music synthesis). Finally, key issues and future questions regarding deep learning applied to audio signal processing are identified.Comment: 15 pages, 2 pdf figure

Whole Word Phonetic Displays for Speech Articulation Training

The main objective of this dissertation is to investigate and develop speech recognition technologies for speech training for people with hearing impairments. During the course of this work, a computer aided speech training system for articulation speech training was also designed and implemented. The speech training system places emphasis on displays to improve children\u27s pronunciation of isolated Consonant-Vowel-Consonant (CVC) words, with displays at both the phonetic level and whole word level. This dissertation presents two hybrid methods for combining Hidden Markov Models (HMMs) and Neural Networks (NNs) for speech recognition. The first method uses NN outputs as posterior probability estimators for HMMs. The second method uses NNs to transform the original speech features to normalized features with reduced correlation. Based on experimental testing, both of the hybrid methods give higher accuracy than standard HMM methods. The second method, using the NN to create normalized features, outperforms the first method in terms of accuracy. Several graphical displays were developed to provide real time visual feedback to users, to help them to improve and correct their pronunciations

Precision language education: a glimpse into a possible future

This is a reflective article on “precision language education”. This concept is derived in part from “precision education” which, in turn, is derived from “precision medicine”. Precision language education heralds a new way of dealing with individual differences by effecting as precise a diagnosis as possible on each language learner, thus triggering specific interventions designed to target and respond to each person’s specific language-learning problems. The article develops the logic of precision language education, including the ways of eliciting and making visible, for both learner and observer, problems and difficulties to be diagnosed and remedied. It then briefly discusses the connection between personalized education and precision education before moving on to offer illustrative examples of precision language education at work which draw on a multiplicity of ways of addressing learning issues, including exploiting neuroplasticity. They include: an answer-evaluation and markup system, a phonetic correction system for three pairs of vowels and a neurological profiling system for guiding the forms of intervention applied. The article concludes with an argument that, in addition to offering a framework for action, precision language education enables the development of a flexible, coherent, “precision” mindset that is of benefit for generating individualized language learning systems to better meet the demands of the highly mobile, globalizing world of the 21st century