Training Multi-Speaker Neural Text-to-Speech Systems using Speaker-Imbalanced Speech Corpora

When the available data of a target speaker is insufficient to train a high quality speaker-dependent neural text-to-speech (TTS) system, we can combine data from multiple speakers and train a multi-speaker TTS model instead. Many studies have shown that neural multi-speaker TTS model trained with a small amount data from multiple speakers combined can generate synthetic speech with better quality and stability than a speaker-dependent one. However when the amount of data from each speaker is highly unbalanced, the best approach to make use of the excessive data remains unknown. Our experiments showed that simply combining all available data from every speaker to train a multi-speaker model produces better than or at least similar performance to its speaker-dependent counterpart. Moreover by using an ensemble multi-speaker model, in which each subsystem is trained on a subset of available data, we can further improve the quality of the synthetic speech especially for underrepresented speakers whose training data is limited.Comment: Submitted to Interspeech 2019, Graz, Austri

Transfer Learning for Speech and Language Processing

Transfer learning is a vital technique that generalizes models trained for one setting or task to other settings or tasks. For example in speech recognition, an acoustic model trained for one language can be used to recognize speech in another language, with little or no re-training data. Transfer learning is closely related to multi-task learning (cross-lingual vs. multilingual), and is traditionally studied in the name of `model adaptation'. Recent advance in deep learning shows that transfer learning becomes much easier and more effective with high-level abstract features learned by deep models, and the `transfer' can be conducted not only between data distributions and data types, but also between model structures (e.g., shallow nets and deep nets) or even model types (e.g., Bayesian models and neural models). This review paper summarizes some recent prominent research towards this direction, particularly for speech and language processing. We also report some results from our group and highlight the potential of this very interesting research field.Comment: 13 pages, APSIPA 201

Introduction to the Special Issue “Speaker and Language Characterization and Recognition: Voice Modeling, Conversion, Synthesis and Ethical Aspects”

International audienceWelcome to this special issue on Speaker and Language Characterization which features, among other contributions, some of the most remarkable ideas presented and discussed at Odyssey 2018: the Speaker and Language Recognition Workshop, held in Les Sables d'Olonne, France, in June 2018. This issue perpetuates the series proposed by ISCA Speaker and language Characterization Special Interest Group in coordination with ISCA Speaker Odyssey workshops [1, 2, 3]. Voice is one of the most casual modalities for natural and intuitive interactions between humans as well as between humans and machines. Voice is also a central part of our identity. Voice-based solutions are currently deployed in a growing variety of applications, including person authentication through automatic speaker verification (ASV). A related technology concerns digital cloning of personal voice characteristics for text-to-speech (TTS) and voice conversion (VC). In the last years, the impressive advancements of the VC/TTS field opened the way for numerous new consumer applications. Especially, VC is offering new solutions for privacy protection. However, VC/TTS also brings the possibility of misuse of the technology in order to spoof ASV systems (for example presentation attacks implemented using voice conversion). As a direct consequence, spoofing countermeasures raises a growing interest during the past years. Moreover, voice is a central part of our identity and is also bringing othe

Text-to-speech system for low-resource language using cross-lingual transfer learning and data augmentation

Deep learning techniques are currently being applied in automated text-to-speech (TTS) systems, resulting in significant improvements in performance. However, these methods require large amounts of text-speech paired data for model training, and collecting this data is costly. Therefore, in this paper, we propose a single-speaker TTS system containing both a spectrogram prediction network and a neural vocoder for the target language, using only 30 min of target language text-speech paired data for training. We evaluate three approaches for training the spectrogram prediction models of our TTS system, which produce mel-spectrograms from the input phoneme sequence: (1) cross-lingual transfer learning, (2) data augmentation, and (3) a combination of the previous two methods. In the cross-lingual transfer learning method, we used two high-resource language datasets, English (24 h) and Japanese (10 h). We also used 30 min of target language data for training in all three approaches, and for generating the augmented data used for training in methods 2 and 3. We found that using both cross-lingual transfer learning and augmented data during training resulted in the most natural synthesized target speech output. We also compare single-speaker and multi-speaker training methods, using sequential and simultaneous training, respectively. The multi-speaker models were found to be more effective for constructing a single-speaker, low-resource TTS model. In addition, we trained two Parallel WaveGAN (PWG) neural vocoders, one using 13 h of our augmented data with 30 min of target language data and one using the entire 12 h of the original target language dataset. Our subjective AB preference test indicated that the neural vocoder trained with augmented data achieved almost the same perceived speech quality as the vocoder trained with the entire target language dataset. Overall, we found that our proposed TTS system consisting of a spectrogram prediction network and a PWG neural vocoder was able to achieve reasonable performance using only 30 min of target language training data. We also found that by using 3 h of target language data, for training the model and for generating augmented data, our proposed TTS model was able to achieve performance very similar to that of the baseline model, which was trained with 12 h of target language data

SLU FOR VOICE COMMAND IN SMART HOME: COMPARISON OF PIPELINE AND END-TO-END APPROACHES

International audienceSpoken Language Understanding (SLU) is typically performedthrough automatic speech recognition (ASR) andnatural language understanding (NLU) in a pipeline. However,errors at the ASR stage have a negative impact on theNLU performance. Hence, there is a rising interest in End-to-End (E2E) SLU to jointly perform ASR and NLU. AlthoughE2E models have shown superior performance to modularapproaches in many NLP tasks, current SLU E2E modelshave still not definitely superseded pipeline approaches.In this paper, we present a comparison of the pipelineand E2E approaches for the task of voice command in smarthomes. Since there are no large non-English domain-specificdata sets available, although needed for an E2E model, wetackle the lack of such data by combining Natural LanguageGeneration (NLG) and text-to-speech (TTS) to generateFrench training data. The trained models were evaluatedon voice commands acquired in a real smart home with severalspeakers. Results show that the E2E approach can reachperformances similar to a state-of-the art pipeline SLU despitea higher WER than the pipeline approach. Furthermore,the E2E model can benefit from artificially generated data toexhibit lower Concept Error Rates than the pipeline baselinefor slot recognition

Towards End-to-End spoken intent recognition in smart home

International audienceVoice based interaction in a smart home has become a feature of many industrial products. These systems react to voice commands, whether it is for answering a question, providing music or turning on the lights. To be efficient, these systems must be able to extract the intent of the user from the voice command. Intent recognition from voice is typically performed through automatic speech recognition (ASR) and intent classification from the transcriptions in a pipeline. However, the errors accumulated at the ASR stage might severely impact the intent classifier. In this paper, we propose an End-to-End (E2E) model to perform intent classification directly from the raw speech input. The E2E approach is thus optimized for this specific task and avoids error propagation. Furthermore, prosodic aspects of the speech signal can be exploited by the E2E model for intent classification (e.g., question vs imperative voice). Experiments on a corpus of voice commands acquired in a real smart home reveal that the state-of-the art pipeline baseline is still superior to the E2E approach. However, using artificial data generation techniques we show that significant improvement to the E2E model can be brought to reach competitive performances. This opens the way to further research on E2E Spoken Language Understanding

Automatic Identification of Emotional Information in Spanish TV Debates and Human-Machine Interactions

Automatic emotion detection is a very attractive field of research that can help build more natural human–machine interaction systems. However, several issues arise when real scenarios are considered, such as the tendency toward neutrality, which makes it difficult to obtain balanced datasets, or the lack of standards for the annotation of emotional categories. Moreover, the intrinsic subjectivity of emotional information increases the difficulty of obtaining valuable data to train machine learning-based algorithms. In this work, two different real scenarios were tackled: human–human interactions in TV debates and human–machine interactions with a virtual agent. For comparison purposes, an analysis of the emotional information was conducted in both. Thus, a profiling of the speakers associated with each task was carried out. Furthermore, different classification experiments show that deep learning approaches can be useful for detecting speakers’ emotional information, mainly for arousal, valence, and dominance levels, reaching a 0.7F1-score.The research presented in this paper was conducted as part of the AMIC and EMPATHIC projects, which received funding from the Spanish Minister of Science under grants TIN2017-85854-C4-3-R and PDC2021-120846-C43 and from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 769872. The first author also received a PhD scholarship from the University of the Basque Country UPV/EHU, PIF17/310