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  • The Inria's Research Teams produce an annual Activity Report presenting their activities and their results of the year. These reports include the team members, the scientific program, the software developed by the team and the new results of the year. The report also describes the grants, contracts and the activities of dissemination and teaching. Finally, the report gives the list of publications of the year.

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Section: New Results

Speech and Audio Processing from the Raw Waveform

State-of-the-art speech technology systems (e.g., ASR and TTS) rely on fixed, hand-crafted features such as mel-filterbanks to preprocess the waveform before the training pipeline. This is at odds with recent work in machine vision where hand-crafter features (SIFT, etc) have been succesfully replaced by features derived from raw pixels trained jointly with a downstream task. In this line of work, we explored how a similar approach could be undertaken for audio and speech processing.

  • In [24], we train a bank of complex filters that operates at the level of the raw speech signal and feeds into a convolutional neural network for phone recognition. These time-domain filterbanks (TD-filterbanks) are initialized as an approximation of MFSC, and then fine-tuned jointly with the remaining convolutional network. We perform phone recognition experiments on TIMIT and show that for several architectures, models trained on TD-filterbanks consistently out-perform their counterparts trained on comparable MFSC. We get our best performance by learning all front-end steps, from pre-emphasis up to averaging. Finally, we observe that the filters at convergence have an asymmetric impulse response while preserving some analyticity.

  • In [25], we study end-to-end systems trained directly from the raw waveform, building on two alternatives for trainable replacements of mel-filterbanks that use a convolutional architecture. The first one is inspired by gammatone filterbanks [4], [9], and the second one by the scattering transform [24]. We propose two modifications to these architectures and systematically compare them to mel-filterbanks, on the Wall Street Journal dataset. The first modification is the addition of an instance normalization layer, which greatly improves on the gammatone-based trainable filterbanks and speeds up the training of the scattering-based filterbanks. The second one relates to the low-pass filter used in these approaches. These modifications consistently improve performances for both ap- proaches, and remove the need for a careful initialization in scattering-based trainable filterbanks. In particular, we show a consistent improvement in word error rate of the trainable filterbanks relatively to comparable mel-filterbanks. It is the first time end-to-end models trained from the raw signal significantly outperform mel-filterbanks on a large vocabulary task under clean recording conditions.

  • Recent progress in deep learning for audio synthesis opens the way to models that directly produce the waveform, shifting away from the traditional paradigm of relying on vocoders or MIDI synthesizers. Despite their successes, current state-of-the-art neural audio synthesizers such as WaveNet and SampleRNN [12], [8] suffer from prohibitive training and inference times because they are based on autoregressive models that generate audio samples one at a time at a rate of 16kHz. In this work [26], we study the more computationally efficient alternative of generating the waveform frame-by-frame with large strides. We present SING, a lightweight neural audio synthesizer for the original task of generating musical notes given desired instrument, pitch and velocity. Our model is trained end-to-end to generate notes from nearly 1000 instruments with a single decoder, thanks to a new loss function that minimizes the distances between the log spectrograms of the generated and target waveforms. On the generalization task of synthesizing notes for pairs of pitch and instrument not seen during training, SING produces audio with significantly improved perceptual quality compared to a state-of-the-art autoencoder based on WaveNet [4] as measured by a Mean Opinion Score (MOS), and is about 32 times faster for training and 2,500 times faster for inference.