61 research outputs found
Joint Learning of Correlated Sequence Labelling Tasks Using Bidirectional Recurrent Neural Networks
The stream of words produced by Automatic Speech Recognition (ASR) systems is
typically devoid of punctuations and formatting. Most natural language
processing applications expect segmented and well-formatted texts as input,
which is not available in ASR output. This paper proposes a novel technique of
jointly modeling multiple correlated tasks such as punctuation and
capitalization using bidirectional recurrent neural networks, which leads to
improved performance for each of these tasks. This method could be extended for
joint modeling of any other correlated sequence labeling tasks.Comment: Accepted in Interspeech 201
Comparing Different Methods for Disfluency Structure Detection
This paper presents a number of experiments focusing on assessing
the performance of different machine learning methods on the identification of disfluencies and their distinct structural regions over speech data. Several machine learning methods have been applied, namely Naive Bayes, Logistic Regression, Classification and Regression Trees (CARTs), J48 and Multilayer Perceptron. Our experiments show that CARTs outperform the other methods on the identification of the distinct structural disfluent regions. Reported experiments are based on audio segmentation and prosodic features, calculated from a corpus of university lectures in European Portuguese, containing about 32h of speech and about 7.7% of disfluencies. The set of features automatically extracted from the forced alignment corpus proved to be discriminant of the regions contained in the production of a disfluency. This work shows that
using fully automatic prosodic features, disfluency structural regions
can be reliably identified using CARTs, where the best results achieved correspond to 81.5% precision, 27.6% recall, and 41.2% F-measure. The best results concern the detection of the interregnum, followed by the detection of the interruption point
Comparing different machine learning approaches for disfluency structure detection in a corpus of university lectures
This paper presents a number of experiments focusing on assessing the performance of different machine learning methods on the identification of disfluencies and their distinct structural regions over speech data. Several machine learning methods have been applied, namely Naive Bayes, Logistic Regression, Classification and Regression Trees (CARTs), J48 and Multilayer Perceptron.
Our experiments show that CARTs outperform the other methods on the identification of the distinct structural disfluent regions. Reported experiments are based on audio segmentation and prosodic features, calculated from a corpus of university lectures in European Portuguese, containing about 32h of speech and about 7.7% of disfluencies. The set of features automatically extracted from the forced alignment corpus proved to be discriminant of the regions contained in the production of a disfluency. This work shows that using fully automatic prosodic features, disfluency structural regions can be reliably identified using CARTs, where the best results achieved correspond to 81.5% precision, 27.6% recall, and 41.2% F-measure. The best results concern the detection of the interregnum, followed by the detection of the interruption point.info:eu-repo/semantics/publishedVersio
End-to-End Speech Recognition and Disfluency Removal with Acoustic Language Model Pretraining
The SOTA in transcription of disfluent and conversational speech has in
recent years favored two-stage models, with separate transcription and cleaning
stages. We believe that previous attempts at end-to-end disfluency removal have
fallen short because of the representational advantage that large-scale
language model pretraining has given to lexical models. Until recently, the
high dimensionality and limited availability of large audio datasets inhibited
the development of large-scale self-supervised pretraining objectives for
learning effective audio representations, giving a relative advantage to the
two-stage approach, which utilises pretrained representations for lexical
tokens. In light of recent successes in large scale audio pretraining, we
revisit the performance comparison between two-stage and end-to-end model and
find that audio based language models pretrained using weak self-supervised
objectives match or exceed the performance of similarly trained two-stage
models, and further, that the choice of pretraining objective substantially
effects a model's ability to be adapted to the disfluency removal task
Sentence boundary detection in chinese broadcast news using conditional random fields and prosodic features
In this paper, we explore the use of prosodic features in sen-tence boundary detection in Chinese broadcast news. The prosodic features include speaker turn, music, pause dura-tion, pitch, energy and speaking rate. Specifically, consider-ing the Chinese tonal effects in pitch trajectory, we propose to use tone-normalized pitch features. Experiments using deci-sion trees demonstrate that the tone-normalized pitch features show superior performance in sentence boundary detection in Chinese broadcast news. Furthermore, feature combination is able to achieve apparent performance improvement by in-tuitive feature interactive rules formed in the decision tree. Pause duration and a tone-normalized pitch feature contribute the most part of the feature usage in the best-performing de-cision tree. Index Terms — sentence boundary detection, sentence segmentation, speech prosody, rich transcription 1
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