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ASR performance prediction on unseen broadcast programs using convolutional neural networks

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HAL Id: hal-02088829

https://hal.archives-ouvertes.fr/hal-02088829

Submitted on 3 Apr 2019

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ASR performance prediction on unseen broadcast programs using convolutional neural networks

Zied Elloumi, Laurent Besacier, Olivier Galibert, Juliette Kahn, Benjamin Lecouteux

To cite this version:

Zied Elloumi, Laurent Besacier, Olivier Galibert, Juliette Kahn, Benjamin Lecouteux. ASR perfor- mance prediction on unseen broadcast programs using convolutional neural networks. ICASSP, 2018, Alberta, Canada. �hal-02088829�

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ASR Performance Prediction on Unseen Broadcast Programs using Convolutional Neural Networks

Zied Elloumi, Laurent Besacier, Olivier Galibert, Juliette Kahn, Benjamin Lecouteux

Contact :

[email protected]

 We used an existing tool named TranscRater for baseline regression approach

 We adapted the TranscRater tool from English to French that requires engineered features to predict the WER performance

 Analysis of predicted WERs

 We presented an evaluation framework for evaluating ASR performance prediction on unseen broadcast programs

 CNNs were very efcient encoding both textual (ASR transcript) and signal to predict WER.

Future work : Analyze the learnt representations of our performance prediction system

 We built our own French ASR system based on the KALDI toolkit

 We used a French data from diferent broadcast collections : ESTER, ETAPE, REPERE, Quaero

 We proposed a new End-to-End performance prediction system based on CNNs.

 Several approaches to encode speech signal

Input : textual, speech signal or the both textual+ speech signal

Output : two proposed methods to predict a continous value ( CNN Softmax and CNN ReLU )

 Distribution of speech turns according to their WER

(a) real (b) Regression

Pred

(c) CNN

Pred

Regression Baseline Regression Baseline

Conclusion Conclusion

Test prediction WER=31.20

59h27 04h42

04h17 100h51

Train prediction WER=22.29

Train Acoustic

Evaluation framework

Evaluation framework Our proposed approach Our proposed approach Abstract

Abstract

2018 IEEE International Conference on Acoustics, Speech and Signal Processing 15–20 April 2018 • Calgary, Alberta, Canada

Automatic Transcription

Feature Extraction SIG LEX LM POS

PP System

 Propose an heterogenous French corpus dedicated to performance prediction task

 Compare two prediction approaches : regression (engineered features) based vs a new strategy based on convolutional neural networks (learnt features).

 The joint use of textual and signal features did not work for the regression baseline while the combination of inputs for CNNs leads to the best WER prediction performance.

 CNN prediction predicts the WER distribution on a collection of speech recordings

Results & Analysis

Results & Analysis

Références

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