Gwénolé Lecorvé

MediaParl: Bilingual mixed language accented speech database

MediaParl is a Swiss accented bilingual database containing recordings in both French and German as they are spoken in Switzerland. The data were recorded at the Valais Parliament. Valais is a bilingual Swiss canton with many local accents and dialects. Therefore, the database contains data with high variability and is suitable to study multilingual, accented and non-native speech recognition as well as language identification and language switch detection. We also define monolingual and mixed language automatic speech recognition and language identification tasks and evaluate baseline systems. The database is publicly available for download.

Automatically finding semantically consistent n-grams to add new words in LVCSR systems

This paper presents a new method to automatically add n-grams containing out-of-vocabulary (OOV) words to a baseline language model (LM), where these n-grams are sought to be grammatically correct and to make sense according to the meaning of OOV words. First, this method consists in determining the word sequences, i.e., n-grams, in which the usage of a given OOV word is the most semantically consistent. Then, conditional probabilities of these n-grams have to be computed. To do this, semantic relations between words are used to assimilate each OOV word to several equivalent in-vocabulary words. Based on these last words, n-grams from the baseline LM are re-used to find the word sequences to be added and to compute their probabilities. After augmenting the vocabulary and launching a recognition process, experiments show that our method results in WER improvements which are comparable to those obtained using a state-of-the-art open vocabulary LM.

Improving TTS with Corpus-Specific Pronunciation Adaptation.

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 corpus-specific phoneme-to-phoneme conditional random fields with a large set of linguistic, phonological, articulatory and acoustic-prosodic 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.