Unit Selection Cost Function Exploration Using an A* Based Text-to-Speech System

Guennec, David; Lolive, Damien

doi:10.1007/978-3-319-10816-2_52

Cited by 11 publications

(5 citation statements)

References 14 publications

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“…neutral] pronunciation. Speech samples were synthesized using the corpusbased TTS system described in [51]. Whatever the pronunciation, the voice corpus is always the TelecomVo training subcorpus.…”

Section: Protocolmentioning

confidence: 99%

Can We Generate Emotional Pronunciations for Expressive Speech Synthesis?

Tahon

Lecorvé

Lolive

2020

IEEE Trans. Affective Comput.

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Abstract-In the field of expressive speech synthesis, a lot of work has been conducted on suprasegmental prosodic features while few has been done on pronunciation variants. However, prosody is highly related to the sequence of phonemes to be expressed. This article raises two issues in the generation of emotional pronunciations for TTS systems. The first issue consists in designing an automatic pronunciation generation method from text, while the second issue addresses the very existence of emotional pronunciations through experiments conducted on emotional speech. To do so, an innovative pronunciation adaptation method which automatically adapts canonical phonemes first to those labeled in the corpus used to create a synthetic voice, then to those labeled in an expressive corpus, is presented. This method consists in training conditional random fields pronunciation models with prosodic, linguistic, phonological and articulatory features. The analysis of emotional pronunciations reveals strong dependencies between prosody and phoneme assimilation or elisions. According to perceptual tests, the double adaptation allows to synthesize expressive speech samples of good quality, but emotion-specific pronunciations are too subtle to be perceived by testers.

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Section: Protocolmentioning

confidence: 99%

Can We Generate Emotional Pronunciations for Expressive Speech Synthesis?

Tahon

Lecorvé

Lolive

2020

IEEE Trans. Affective Comput.

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“…Audiobook corpus is extracted from a French audio book [11]. The reader is a professional male French actor with a very low voice (91 Hz, 77 st).…”

Section: Speech Corporamentioning

confidence: 99%

Perception of Expressivity in TTS: Linguistics, Phonetics or Prosody?

Tahon

Lecorvé

Lolive

et al. 2017

Statistical Language and Speech Processing

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Actually a lot of work on expressive speech focus on acoustic models and prosody variations. However, in expressive Text-to-Speech (TTS) systems, prosody generation strongly relies on the sequence of phonemes to be expressed and also to the words below these phonemes. Consequently, linguistic and phonetic cues play a significant role in the perception of expressivity. In previous works, we proposed a statistical corpus-specific framework which adapts phonemes derived from an automatic phonetizer to the phonemes as labelled in the TTS speech corpus. This framework allows to synthesize good quality but neutral speech samples. The present study goes further in the generation of expressive speech by predicting not only corpus-specific but also expressive pronunciation. It also investigates the shared impacts of linguistics, phonetics and prosody, these impacts being evaluated through different French neutral and expressive speech collected with different speaking styles and linguistic content and expressed under diverse emotional states. Perception tests show that expressivity is more easily perceived when linguistics, phonetics and prosody are consistent. Linguistics seems to be the strongest cue in the perception of expressivity, but phonetics greatly improves expressiveness when combined with and adequate prosody.

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“…Utterances were randomly selected by subsampling the validation set according to the PER distribution between canonical and realized pronunciations. Speech samples were synthesized using the corpus-based TTS system described in [24] and also with HTS v2.2 with standard features [25]. Some examples extracted from the listening test are available on the team website 1 .…”

Section: Synthesized Speech Evaluationmentioning

confidence: 99%

Improving TTS with Corpus-Specific Pronunciation Adaptation

Tahon¹,

Qader²,

Lecorvé³

et al. 2016

Interspeech 2016

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Text-to-speech (TTS) systems are built on speech corpora which are labeled with carefully checked and segmented phonemes. However, phoneme sequences generated by automatic grapheme-to-phoneme converters during synthesis are usually inconsistent with those from the corpus, thus leading to poor quality synthetic speech signals. To solve this problem, the present work aims at adapting automatically generated pronunciations to the corpus. The main idea is to train corpusspecific phoneme-to-phoneme conditional random fields with a large set of linguistic, phonological, articulatory and acousticprosodic features. Features are first selected in cross-validation condition, then combined to produce the final best feature set. Pronunciation models are evaluated in terms of phoneme error rate and through perceptual tests. Experiments carried out on a French speech corpus show an improvement in the quality of speech synthesis when pronunciation models are included in the phonetization process. Appart from improving TTS quality, the presented pronunciation adaptation method also brings interesting perspectives in terms of expressive speech synthesis.Experiments were carried out on a French speech corpus dedicated to interactive vocal system TTS. As such, this corpus covers all diphonemes present in French and comprises most

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Unit Selection Cost Function Exploration Using an A* Based Text-to-Speech System

Cited by 11 publications

References 14 publications

Can We Generate Emotional Pronunciations for Expressive Speech Synthesis?

Can We Generate Emotional Pronunciations for Expressive Speech Synthesis?

Perception of Expressivity in TTS: Linguistics, Phonetics or Prosody?

Improving TTS with Corpus-Specific Pronunciation Adaptation

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