In this paper, we are interested in exploiting textual and acoustic data of an utterance for the speech emotion classification task. The baseline approach models the information from audio and text independently using two deep neural networks (DNNs). The outputs from both the DNNs are then fused for classification. As opposed to using knowledge from both the modalities separately, we propose a framework to exploit acoustic information in tandem with lexical data. The proposed framework uses two bi-directional long short-term memory (BLSTM) for obtaining hidden representations of the utterance. Furthermore, we propose an attention mechanism, referred to as the multi-hop, which is trained to automatically infer the correlation between the modalities. The multi-hop attention first computes the relevant segments of the textual data corresponding to the audio signal. The relevant textual data is then applied to attend parts of the audio signal. To evaluate the performance of the proposed system, experiments are performed in the IEMOCAP dataset. Experimental results show that the proposed technique outperforms the state-of-the-art system by 6.5 relative improvement in terms of weighted accuracy.
Along with classical algorithms based models such as support vector machine (SVM), hidden markov model (HMM) and decision tree @cite_18 @cite_3 @cite_10 , various neural network architectures have been recently introduced for the speech emotion recognition task. For example, convolutional neural network (CNN)-based models were trained on spectrograms or audio features such as mel-frequency cepstral coefficients (MFCCs) and low-level descriptors (LLDs) @cite_9 @cite_20 @cite_1 . More complex models such as @cite_14 were designed to better learn nonlinear decision boundaries of emotional speech and achieved the best-recorded performance in audio modality models on IEMOCAP dataset @cite_22 . Several neural network models with attention mechanism have been proposed to efficiently focus on a prominent part of speech and learn temporal dependency within whole utterance @cite_17 @cite_24 .
@cite_18 @cite_14 @cite_22 @cite_9 @cite_1 @cite_3 @cite_24 @cite_10 @cite_20 @cite_17
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In hybrid hidden Markov model artificial neural networks (HMM ANN) automatic speech recognition (ASR) system, the phoneme class conditional probabilities are estimated by first extracting acoustic features from the speech signal based on prior knowledge such as, speech perception or and speech production knowledge, and, then modeling the acoustic features with an ANN. Recent advances in machine learning techniques, more specifically in the field of image processing and text processing, have shown that such divide and conquer strategy (i.e., separating feature extraction and modeling steps) may not be necessary. Motivated from these studies, in the framework of convolutional neural networks (CNNs), this paper investigates a novel approach, where the input to the ANN is raw speech signal and the output is phoneme class conditional probability estimates. On TIMIT phoneme recognition task, we study different ANN architectures to show the benefit of CNNs and compare the proposed approach against conventional approach where, spectral-based feature MFCC is extracted and modeled by a multilayer perceptron. Our studies show that the proposed approach can yield comparable or better phoneme recognition performance when compared to the conventional approach. It indicates that CNNs can learn features relevant for phoneme classification automatically from the raw speech signal. 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 We propose a real-time Convolutional Neural Network model for speech emotion detection. Our model is trained from raw audio on a small dataset of TED talks speech data, manually annotated into three emotion classes: “Angry”, “Happy” and “Sad”. It achieves an average accuracy of 66.1 , 5 higher than a feature-based SVM baseline, with an evaluation time of few hundred milliseconds. We also provide an in-depth model visualization and analysis. We show how our neural network effectively activates during the speech sections of the waveform regardless of the emotion, ignoring the silence parts which do not contain information. On the frequency domain the CNN filters distribute throughout all the spectrum range, with higher concentration around the average pitch range related to that emotion. Each filter also activates at multiple frequency intervals, presumably due to the additional contribution of amplitude-related feature learning. Our work will allow faster and more accurate emotion detection modules for human-machine empathetic dialog systems and other related applications. Audio-visual speech recognition (AVSR) system is thought to be one of the most promising solutions for reliable speech recognition, particularly when the audio is corrupted by noise. However, cautious selection of sensory features is crucial for attaining high recognition performance. In the machine-learning community, deep learning approaches have recently attracted increasing attention because deep neural networks can effectively extract robust latent features that enable various recognition algorithms to demonstrate revolutionary generalization capabilities under diverse application conditions. This study introduces a connectionist-hidden Markov model (HMM) system for noise-robust AVSR. First, a deep denoising autoencoder is utilized for acquiring noise-robust audio features. By preparing the training data for the network with pairs of consecutive multiple steps of deteriorated audio features and the corresponding clean features, the network is trained to output denoised audio features from the corresponding features deteriorated by noise. Second, a convolutional neural network (CNN) is utilized to extract visual features from raw mouth area images. By preparing the training data for the CNN as pairs of raw images and the corresponding phoneme label outputs, the network is trained to predict phoneme labels from the corresponding mouth area input images. Finally, a multi-stream HMM (MSHMM) is applied for integrating the acquired audio and visual HMMs independently trained with the respective features. By comparing the cases when normal and denoised mel-frequency cepstral coefficients (MFCCs) are utilized as audio features to the HMM, our unimodal isolated word recognition results demonstrate that approximately 65 word recognition rate gain is attained with denoised MFCCs under 10 dB signal-to-noise-ratio (SNR) for the audio signal input. Moreover, our multimodal isolated word recognition results utilizing MSHMM with denoised MFCCs and acquired visual features demonstrate that an additional word recognition rate gain is attained for the SNR conditions below 10 dB.