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The paper presents a mechanism to perform speaker adaptation in speech synthesis based on deep neural networks (DNNs). The mechanism extracts speaker identification vectors, socalled d-vectors, from the training speakers and uses them jointly with the linguistic features to train a multi-speaker DNNbased text-to-speech synthesizer (DNN-TTS). The d-vectors are derived by applying principal component analysis (PCA) on the bottleneck features of a speaker classifier network. At the adaptation stage, three variants are explored: (1) d-vectors calculated using data from the target speaker, or (2) d-vectors calculated as a weighted sum of d-vectors from training speakers, or (3) d-vectors calculated as an average of the above two approaches. The proposed method of unsupervised adaptation using the d-vector is compared with the commonly used i-vector based approach for speaker adaptation. Listening tests show that: (1) for speech quality, the d-vector based approach is significantly preferred over the i-vector based approach. All the d-vector variants perform similar for speech quality; (2) for speaker similarity, both d-vector and i-vector based adaptation were found to perform similar, except a small significant preference for the d-vector calculated as an average over the i-vector.

In this paper we introduce a new cepstral coefficient extraction method based on an intelligibility measure for speech in noise, the Glimpse Proportion measure. This new method aims to increase the intelligibility of speech in noise by modifying the clean speech, and has applications in scenarios such as public announcement and car navigation systems. We first explain how the Glimpse Proportion measure operates and further show how we approximated it to integrate it into an existing spectral envelope parameter extraction method commonly used in the HMM-based speech synthesis framework. We then demonstrate how this new method changes the modelled spectrum according to the characteristics of the noise and show results for a listening test with vocoded and HMM-based synthetic speech. The test indicates that the proposed method can significantly improve intelligibility of synthetic speech in speech shaped noise.Index Terms-cepstral coefficient extraction, objective measure for speech intelligibility, Lombard speech, HMM-based speech synthesis

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This paper describes speech intelligibility enhancement for hidden Markov model (HMM) generated synthetic speech in noise. We present a method for modifying the Mel cepstral coefficients generated by statistical parametric models that have been trained on plain speech. We update these coefficients such that the Glimpse Proportion -an objective measure of the intelligibility of speech in noise -increases, while keeping the speech energy fixed. An acoustic analysis reveals that the modified speech is boosted in the region 1-4kHz, particularly for vowels, nasals and approximants. Results from listening tests employing speech-shaped noise show that the modified speech is as intelligible as a synthetic voice trained on plain speech whose duration, Mel cepstral coefficients and excitation signal parameters have been adapted to Lombard speech from the same speaker. Our proposed method does not require these additional recordings of Lombard speech. In the presence of a competing talker, both modification and adaptation of spectral coefficients give more modest gains.

Research on speech synthesis area has made great progress recently, perhaps motivated by its numerous applications, of which text-to-speech converters and dialog systems are examples. Several improvements have been reported in the technical literature related to existing state-of-the-art techniques as well as in the development of new ideas related to the alteration of voice characteristics, with their eventual application to different languages. Nevertheless, in spite of the attention that the speech synthesis field has been receiving, the technique which employs unit selection and concatenation of waveform segments still remains as the most popular approach among those available nowadays. In this paper, we report how a synthesizer for the Brazilian Portuguese language was constructed according to a technique in which the speech waveform is generated through parameters directly determined from Hidden Markov Models. When compared with systems based on unit selection and concatenation, the proposed synthesizer presents the advantage of being trainable, with the utilization of contextual factors including information related to different levels of the following acoustic units: phones, syllables, words, phrases and utterances. Such information is brought into effect through a set of questions for context-clustering. Thus, both the spectral and the prosodic characteristics of the system are managed by decision-trees generated for each one of the following parameters: mel-cepstral coefficients, fundamental frequency and state durations. As a typical characteristic of the technique based on Hidden Markov Models, synthesized speech with quality comparable to commercial applications built under the unit selection and concatenation approach can be obtained even from a database as small as eighteen minutes of speech. This was tested by a subjective comparison of samples from the synthesizer in question and other systems currently available for Brazilian Portuguese.

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