SEGAN: Speech Enhancement Generative Adversarial Network

Current speech enhancement techniques operate on the spectral domain and/or exploit some higher-level feature. The majority of them tackle a limited number of noise conditions and rely on first-order statistics. To circumvent these issues, deep networks are being increasingly used, thanks to their ability to learn complex functions from large example sets. In this work, we propose the use of generative adversarial networks for speech enhancement. In contrast to current techniques, we operate at the waveform level, training the model end-to-end, and incorporate 28 speakers and 40 different noise conditions into the same model, such that model parameters are shared across them. We evaluate the proposed model using an independent, unseen test set with two speakers and 20 alternative noise conditions. The enhanced samples confirm the viability of the proposed model, and both objective and subjective evaluations confirm the effectiveness of it. With that, we open the exploration of generative architectures for speech enhancement, which may progressively incorporate further speech-centric design choices to improve their performance.Comment: 5 pages, 4 figures, accepted in INTERSPEECH 201

Tecnologías del habla : conversión de texto a voz

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Language modeling using X-grams

In this paper, an extension of n-grams, called x-grams, is proposed. In this extension, the memory of the model (n) is not fixed a priori. Instead, large memories are accepted first, and merging criteria are then applied to reduce the complexity and to ensure reliable estimations. The results show how the perplexity obtained with x-grams is smaller than that of n-grams. Furthermore, the complexity is smaller than trigrams and can become close to bigrams.Peer ReviewedPostprint (published version

Duration modeling with expanded HMM applied to speech recognition

The occupancy of the HMM states is modeled by means of a Markov chain. A linear estimator is introduced to compute the probabilities of the Markov chain. The distribution function (DF) represents accurately the observed data. Representing the DF as a Markov chain allows the use of standard HMM recognizers. The increase of complexity is negligible in training and strongly limited during recognition. Experiments performed on acoustic-phonetic decoding shows how the phone recognition rate increases from 60.6 to 61.1. Furthermore, on a task of database inquires, where phones are used as subword units, the correct word rate increases from 88.2 to 88.4.Peer ReviewedPostprint (published version

Prosodic and spectral iVectors for expressive speech synthesis

This work presents a study on the suitability of prosodic andacoustic features, with a special focus on i-vectors, in expressivespeech analysis and synthesis. For each utterance of two dif-ferent databases, a laboratory recorded emotional acted speech,and an audiobook, several prosodic and acoustic features are ex-tracted. Among them, i-vectors are built not only on the MFCCbase, but also on F0, power and syllable durations. Then, un-supervised clustering is performed using different feature com-binations. The resulting clusters are evaluated calculating clus-ter entropy for labeled portions of the databases. Additionally,synthetic voices are trained, applying speaker adaptive training,from the clusters built from the audiobook. The voices are eval-uated in a perceptual test where the participants have to edit anaudiobook paragraph using the synthetic voices.The objective results suggest that i-vectors are very use-ful for the audiobook, where different speakers (book charac-ters) are imitated. On the other hand, for the laboratory record-ings, traditional prosodic features outperform i-vectors. Also,a closer analysis of the created clusters suggest that differentspeakers use different prosodic and acoustic means to conveyemotions. The perceptual results suggest that the proposed i-vector based feature combinations can be used for audiobookclustering and voice training.Peer ReviewedPostprint (published version

Nativization of English words in Spanish using analogy

Nowadays modern speech technologies need to be flexible and adaptable to any framework. Mass media globalization introduces the challenge of multilingualism into most popular speech applications such as text-to-speech synthesis and automatic speech recognition. Mixed-language texts vary in their nature and when processed, some essential characteristics ought to be considered. In Spain, the usage of English and other foreign origin words is growing as well as in other countries. The particularity of the peninsular Spanish is that there is a tendency to nativized foreign words pronunciation so that they fit in properly into Spanish phonetics. In this work our goal was to approach the nativization challenge by data-driven methods, since they are transferable to other languages and do not yield in performance. Training and test corpora for nativization were manually crafted and the experiments were carried out using pronunciation by analogy. The results obtained were encouraging and proved that even a small training corpus of 1000 words allows obtaining a higher level of intelligibility for English inclusions in Spanish utterances.Peer ReviewedPostprint (published version

Exploring efficient neural architectures for linguistic-acoustic mapping in text-to-speech

Conversion from text to speech relies on the accurate mapping from linguistic to acoustic symbol sequences, for which current practice employs recurrent statistical models such as recurrent neural networks. Despite the good performance of such models (in terms of low distortion in the generated speech), their recursive structure with intermediate affine transformations tends to make them slow to train and to sample from. In this work, we explore two different mechanisms that enhance the operational efficiency of recurrent neural networks, and study their performance–speed trade-off. The first mechanism is based on the quasi-recurrent neural network, where expensive affine transformations are removed from temporal connections and placed only on feed-forward computational directions. The second mechanism includes a module based on the transformer decoder network, designed without recurrent connections but emulating them with attention and positioning codes. Our results show that the proposed decoder networks are competitive in terms of distortion when compared to a recurrent baseline, whilst being significantly faster in terms of CPU and GPU inference time. The best performing model is the one based on the quasi-recurrent mechanism, reaching the same level of naturalness as the recurrent neural network based model with a speedup of 11.2 on CPU and 3.3 on GPU.Peer ReviewedPostprint (published version