507 research outputs found
Prosodic Event Recognition using Convolutional Neural Networks with Context Information
This paper demonstrates the potential of convolutional neural networks (CNN)
for detecting and classifying prosodic events on words, specifically pitch
accents and phrase boundary tones, from frame-based acoustic features. Typical
approaches use not only feature representations of the word in question but
also its surrounding context. We show that adding position features indicating
the current word benefits the CNN. In addition, this paper discusses the
generalization from a speaker-dependent modelling approach to a
speaker-independent setup. The proposed method is simple and efficient and
yields strong results not only in speaker-dependent but also
speaker-independent cases.Comment: Interspeech 2017 4 pages, 1 figur
Multimodal Speech Emotion Recognition Using Audio and Text
Speech emotion recognition is a challenging task, and extensive reliance has
been placed on models that use audio features in building well-performing
classifiers. In this paper, we propose a novel deep dual recurrent encoder
model that utilizes text data and audio signals simultaneously to obtain a
better understanding of speech data. As emotional dialogue is composed of sound
and spoken content, our model encodes the information from audio and text
sequences using dual recurrent neural networks (RNNs) and then combines the
information from these sources to predict the emotion class. This architecture
analyzes speech data from the signal level to the language level, and it thus
utilizes the information within the data more comprehensively than models that
focus on audio features. Extensive experiments are conducted to investigate the
efficacy and properties of the proposed model. Our proposed model outperforms
previous state-of-the-art methods in assigning data to one of four emotion
categories (i.e., angry, happy, sad and neutral) when the model is applied to
the IEMOCAP dataset, as reflected by accuracies ranging from 68.8% to 71.8%.Comment: 7 pages, Accepted as a conference paper at IEEE SLT 201
Speech Emotion Recognition Using Multi-hop Attention Mechanism
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.Comment: 5 pages, Accepted as a conference paper at ICASSP 2019 (oral
presentation
Deep Learning for Audio Signal Processing
Given the recent surge in developments of deep learning, this article
provides a review of the state-of-the-art deep learning techniques for audio
signal processing. Speech, music, and environmental sound processing are
considered side-by-side, in order to point out similarities and differences
between the domains, highlighting general methods, problems, key references,
and potential for cross-fertilization between areas. The dominant feature
representations (in particular, log-mel spectra and raw waveform) and deep
learning models are reviewed, including convolutional neural networks, variants
of the long short-term memory architecture, as well as more audio-specific
neural network models. Subsequently, prominent deep learning application areas
are covered, i.e. audio recognition (automatic speech recognition, music
information retrieval, environmental sound detection, localization and
tracking) and synthesis and transformation (source separation, audio
enhancement, generative models for speech, sound, and music synthesis).
Finally, key issues and future questions regarding deep learning applied to
audio signal processing are identified.Comment: 15 pages, 2 pdf figure
Infant Cry Signal Processing, Analysis, and Classification with Artificial Neural Networks
As a special type of speech and environmental sound, infant cry has been a growing research area covering infant cry reason classification, pathological infant cry identification, and infant cry detection in the past two decades. In this dissertation, we build a new dataset, explore new feature extraction methods, and propose novel classification approaches, to improve the infant cry classification accuracy and identify diseases by learning infant cry signals.
We propose a method through generating weighted prosodic features combined with acoustic features for a deep learning model to improve the performance of asphyxiated infant cry identification. The combined feature matrix captures the diversity of variations within infant cries and the result outperforms all other related studies on asphyxiated baby crying classification. We propose a non-invasive fast method of using infant cry signals with convolutional neural network (CNN) based age classification to diagnose the abnormality of infant vocal tract development as early as 4-month age. Experiments discover the pattern and tendency of the vocal tract changes and predict the abnormality of infant vocal tract by classifying the cry signals into younger age category. We propose an approach of generating hybrid feature set and using prior knowledge in a multi-stage CNNs model for robust infant sound classification. The dominant and auxiliary features within the set are beneficial to enlarge the coverage as well as keeping a good resolution for modeling the diversity of variations within infant sound and the experimental results give encouraging improvements on two relative databases. We propose an approach of graph convolutional network (GCN) with transfer learning for robust infant cry reason classification. Non-fully connected graphs based on the similarities among the relevant nodes are built to consider the short-term and long-term effects of infant cry signals related to inner-class and inter-class messages. With as limited as 20% of labeled training data, our model outperforms that of the CNN model with 80% labeled training data in both supervised and semi-supervised settings. Lastly, we apply mel-spectrogram decomposition to infant cry classification and propose a fusion method to further improve the infant cry classification performance
Dialogue Act Modeling for Automatic Tagging and Recognition of Conversational Speech
We describe a statistical approach for modeling dialogue acts in
conversational speech, i.e., speech-act-like units such as Statement, Question,
Backchannel, Agreement, Disagreement, and Apology. Our model detects and
predicts dialogue acts based on lexical, collocational, and prosodic cues, as
well as on the discourse coherence of the dialogue act sequence. The dialogue
model is based on treating the discourse structure of a conversation as a
hidden Markov model and the individual dialogue acts as observations emanating
from the model states. Constraints on the likely sequence of dialogue acts are
modeled via a dialogue act n-gram. The statistical dialogue grammar is combined
with word n-grams, decision trees, and neural networks modeling the
idiosyncratic lexical and prosodic manifestations of each dialogue act. We
develop a probabilistic integration of speech recognition with dialogue
modeling, to improve both speech recognition and dialogue act classification
accuracy. Models are trained and evaluated using a large hand-labeled database
of 1,155 conversations from the Switchboard corpus of spontaneous
human-to-human telephone speech. We achieved good dialogue act labeling
accuracy (65% based on errorful, automatically recognized words and prosody,
and 71% based on word transcripts, compared to a chance baseline accuracy of
35% and human accuracy of 84%) and a small reduction in word recognition error.Comment: 35 pages, 5 figures. Changes in copy editing (note title spelling
changed
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