Deep learning is currently playing a crucial role toward higher levels of artificial intelligence. This paradigm allows neural networks to learn complex and abstract representations, that are progressively obtained by combining simpler ones. Nevertheless, the internal "black-box" representations automatically discovered by current neural architectures often suffer from a lack of interpretability, making of primary interest the study of explainable machine learning techniques. This paper summarizes our recent efforts to develop a more interpretable neural model for directly processing speech from the raw waveform. In particular, we propose SincNet, a novel Convolutional Neural Network (CNN) that encourages the first layer to discover more meaningful filters by exploiting parametrized sinc functions. In contrast to standard CNNs, which learn all the elements of each filter, only low and high cutoff frequencies of band-pass filters are directly learned from data. This inductive bias offers a very compact way to derive a customized filter-bank front-end, that only depends on some parameters with a clear physical meaning. Our experiments, conducted on both speaker and speech recognition, show that the proposed architecture converges faster, performs better, and is more interpretable than standard CNNs.
Several works have recently explored the use of low-level speech representations to process audio and speech with CNNs. Most prior attempts exploit magnitude spectrogram features @cite_29 @cite_9 @cite_10 @cite_59 @cite_47 @cite_28 . Although spectrograms retain more information than standard hand-crafted features, their design still requires careful tuning of some crucial hyper-parameters, such as the duration, overlap, and typology of the frame window, as well as the number of frequency bins. For this reason, a more recent trend is to directly learn from raw waveforms, thus completely avoiding any feature extraction step. This approach has shown promise in speech @cite_53 @cite_11 @cite_7 @cite_64 @cite_20 , including emotion tasks @cite_30 , speaker recognition @cite_60 , spoofing detection @cite_48 , and speech synthesis @cite_42 @cite_2 .
@cite_30 @cite_64 @cite_7 @cite_60 @cite_28 @cite_48 @cite_9 @cite_29 @cite_53 @cite_42 @cite_59 @cite_2 @cite_47 @cite_10 @cite_20 @cite_11
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Speaker verification systems traditionally extract and model cepstral features or filter bank energies from the speech signal. In this paper, inspired by the success of neural network-based approaches to model directly raw speech signal for applications such as speech recognition, emotion recognition and anti-spoofing, we propose a speaker verification approach where speaker discriminative information is directly learned from the speech signal by: (a) first training a CNN-based speaker identification system that takes as input raw speech signal and learns to classify on speakers (unknown to the speaker verification system); and then (b) building a speaker detector for each speaker in the speaker verification system by replacing the output layer of the speaker identification system by two outputs (genuine, impostor), and adapting the system in a discriminative manner with enrollment speech of the speaker and impostor speech data. Our investigations on the Voxforge database shows that this approach can yield systems competitive to state-of-the-art systems. An analysis of the filters in the first convolution layer shows that the filters give emphasis to information in low frequency regions (below 1000 Hz) and implicitly learn to model fundamental frequency information in the speech signal for speaker discrimination. Albeit recent progress in speaker verification generates powerful models, malicious attacks in the form of spoofed speech, are generally not coped with. Recent results in ASVSpoof2015 and BTAS2016 challenges indicate that spoof-aware features are a possible solution to this problem. Most successful methods in both challenges focus on spoof-aware features, rather than focusing on a powerful classifier. In this paper we present a novel raw waveform based deep model for spoofing detection, which jointly acts as a feature extractor and classifier, thus allowing it to directly classify speech signals. This approach can be considered as an end-to-end classifier, which removes the need for any pre- or post-processing on the data, making training and evaluation a streamlined process, consuming less time than other neural-network based approaches. The experiments on the BTAS2016 dataset show that the system performance is significantly improved by the proposed raw waveform convolutional long short term neural network (CLDNN), from the previous best published 1.26 half total error rate (HTER) to the current 0.82 HTER. Moreover it shows that the proposed system also performs well under the unknown (RE-PH2-PH3,RE-LPPH2-PH3) conditions. Deep learning is progressively gaining popularity as a viable alternative to i-vectors for speaker recognition. Promising results have been recently obtained with Convolutional Neural Networks (CNNs) when fed by raw speech samples directly. Rather than employing standard hand-crafted features, the latter CNNs learn low-level speech representations from waveforms, potentially allowing the network to better capture important narrow-band speaker characteristics such as pitch and formants. Proper design of the neural network is crucial to achieve this goal.This paper proposes a novel CNN architecture, called SincNet, that encourages the first convolutional layer to discover more meaningful filters. SincNet is based on parametrized sinc functions, which implement band-pass filters. In contrast to standard CNNs, that learn all elements of each filter, only low and high cutoff frequencies are directly learned from data with the proposed method. This offers a very compact and efficient way to derive a customized filter bank specifically tuned for the desired application.Our experiments, conducted on both speaker identification and speaker verification tasks, show that the proposed architecture converges faster and performs better than a standard CNN on raw waveforms. Abstract Automaticspeechrecognitionsystemstypicallymodeltherela-tionship between the acoustic speech signal and the phones intwo separate steps: feature extraction and classier training. Inourrecentworks, wehaveshownthat, intheframeworkofcon-volutionalneuralnetworks(CNN),therelationshipbetweentheraw speech signal and the phones can be directly modeled andASR systems competitive to standard approach can be built. Inthis paper, we rst analyze and show that, between the rst twoconvolutional layers, the CNN learns (in parts) and models thephone-specic spectral envelope information of 2-4 ms speech.Given that we show that the CNN-based approach yields ASRtrends similar to standard short-term spectral based ASR sys-tem under mismatched (noisy) conditions, with the CNN-basedapproach being more robust.Index Terms: automatic speech recognition, convolutionalneural networks, raw signal, robust speech recognition. 1. Introduction State-of-the-art automatic speech recognition (ASR) systemstypically model the relationship between the acoustic speechsignal and the phones in two separate steps, which are op-timized in an independent manner [1]. In a rst step, thespeech signal is transformed into features, usually composed ofa dimensionality reduction phase and an information selectionphase, based on the task-specic knowledge of the phenomena.These two phases have been carefully hand-crafted, leading tostate-of-the-art features such as Mel frequency cepstral coef-cients(MFCCs)orperceptuallinearpredictioncepstralfeatures(PLPs). In a second step, the likelihood of subword units suchas, phonemes is estimated using generative models or discrimi-native models.In recent years, in the hybrid HMM ANN framework [1],there has been growing interests in using intermediate rep-resentations instead of conventional features, such as cepstral-based features, as input for neural networks-based systems.ANNs with deep learning architectures, more precisely, deepneural networks (DNNs) [2, 3], which can yield better systemthan a single hidden layer MLP have been proposed to addressvarious aspects of acoustic modeling. More specically, useof context-dependent phonemes [4, 5]; use of spectral featuresas opposed to cepstral features [6, 7]; CNN-based system withMel lter bank energies as input [8, 9, 10]; combination of dif-ferent features [11], to name a few. Features learning from therawspeechsignalusingneuralnetworks-basedsystemshasalsobeen investigated in [12]. In all these approaches, the features