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Micai 13 contextualized practical speech

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Information about Micai 13 contextualized practical speech
Technology

Published on March 1, 2014

Author: GrupoGolem

Source: slideshare.net

Description

Talk given in MICAI 2013: Practical Speech Recognition for Contextualized Service Robots
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Departamento de Ciencias de la Computación Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas Universidad Nacional Autónoma de México Practical Speech Recognition for Contextualized Service Robots Ivan Meza, Caleb Rascón and Luis Pineda http://golem.iimas.unam.mx/ GrupoGolem

Service robots ● Our future butlers ● They are task oriented ○ Clean up a room ○ Play a game ● ● ● ● Interaction with spoken language They work in noisy environments Microphone is not close to the speaker Poor speech recognition

Proposal ● Improve the system on four aspects ● Contextualized recogniser ● Prompting strategies ● Recovery strategies ● Audio calibration

I. Contextualized recognition ● Use specific language models for the given expectations ■ YES: yes, okay, all right ■ NO: no, don’t, do not ■ NAVIGATE: go to the kitchen, go to the living room, go to the bedroom

ASR module

II. Prompting strategies ● Let know the user when to speak ■ Beep sound ● Speaker volume monitor ■ Could you speak louder or softer

III. Recovery strategy ● Let know the user when something went wrong ■ could you repeat? ■ i can’t hear you well, could you repeat ■ sorry, i’m a little deaf

IV. Calibration of audio setting ● Hardware ■ 1 directional microphone ■ 1 USB interface with 4 channels ■ 2 speakers ● Calibration of SNR in situ ■ For background noise -58dB ■ SNR set to 20 dB

Corpus evaluation ● Logs from the robot performing RoboCup tasks ■ ■ ■ ■ ■ ■ ■ 2 years interactions in lab and competition 1,439 utterances 2,472 tokens 120 types 11 tasks 9 of 11 tasks are contextualized 14 language models

Contextualized recognition We measure WER (the lower the better) ● With a unique LM for all tasks: 53.84% ● With task-based LM: 28.28% ● With contextualized: 23.42% 17.2% relative error reduction

Beep sound ● 79 utterances were recorded without the beep sound ■ Without beeps 55.86% ■ With beeps 39.75% ■ With beeps full 53.72% 30%-4% Relative error reduction

Usage of SoundLoc System ● We measure usage ■ 174 times could have been triggered ■ 21 soft speech ■ 4 louder 14.36% of the times

Recovery strategy ● We measure usage ■ 504 times could have been triggered ■ 85 times activated 16.87% of the times

Conclusions ● These strategies help to improve in small amounts the performance ● Together they allow practical speech recognition on a service robot

Thank you ● ¿Questions?

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