speech recognition ece5526 wilson burgos. outline introduction objective existing solutions...

uSpeak2MeEsol Android Tool

Speech Recognition ECE5526

Wilson Burgos

OutlineIntroductionObjectiveExisting SolutionsImplementationTest and ResultConclusion

IntroductionLots of $$$ are spent annually to improve

language skills for non native speakers.Classes for ESOL (English Speakers of other

languages) Lack of effective toolsSpeech recognition can help us in some areas

ObjectiveCreate a tool to help people learn to speak

English correctly in an effective way.Engage people using new technology

(Smartphone's) Using pocketsphinx, android and Text-to-

speech technologySimple and intuitive to useFun

Existing SolutionsEyeSpeak - http://www.eyespeakenglish.com

Pros Uses Native Speakers Pronunciation, pitch, timing & loudness

Cons Difficult to use Runs only on windows

Concept of OperationThe user from the main menu can start the

gameThe game screen must lead the user through

a series of words and log the number of positive responses (the score).

Each word has a corresponding graphic to display. For example, the game might show the user a picture of a mountain

The user has at most 30 seconds to respond

Development EnvironmentEclipse IDE with Android pluginCygwin Emulator

QEMU-based ARM emulatorRuns the same image as the deviceLimitations

No Camera support

Development EnvironmentActual Device

ImplementationUsing Java with the Android SDKPocketsphinx

Lightweight speech recognition decoder library Implemented in C

Android Architecture

Application Building BlocksActivityIntentReceiverServiceContentProvider

Application Architecture

ImplementationQuizGameActivity

The screen at the heart of the application—the game play screen.

This screen prompts the user to answer a series of trivia questions and stores the resulting score information

Uset Text-to-Speech technology to speak word if in simple mode

Implementation

RecognizerTask AudioTask

PocketSphinx VUMeter

ImplementationRecognizerTask

Interfaces directly to the pocketsphinx library using JNI calls

The Java Native Interface (JNI) enables the integration of code written in the Java programming language with code written in other languages such as C and C

Consumes data from the audio queue, produced by the AudioTask

Calls process_rawScoring

Based on positive detection of the utterance

ImplementationAudioTask

Interfaces directly to the audio peripherals to gather data

Format Sample Rate 8000Hz, 16Bit PCM, 8192k buffer

PocketSphinxVery limited documentationPackaged the pocketsphinx into a shared

library Created java shared library counterpart (jar)

To be added to the android applicationCompiled using cygwinInitialized with custom dictionary and

language modelSpeak2me.dicSpeak2me.lm

Loaded at startup from java code

LimitationsHardware memory

In the sphinx4 demos the recognizer was active all the time gathering data. When running in the device the AudioRecord buffer fills up preventing the recognizer to be active all the time.

Game needs to be responsive, how to solve this problem?

LimitationsHardware memory

The VUMeter class calculates the energy of the sampled data, removing the DC offset with a filter.

Detection logic was added to trigger end of utterance automatically with configurable lock/unlock thresholds

The game timer automatically starts the recognizer after every given word

Device SpeedTo improve detection the application uses the

partial results to determine if a match has been found, doesn’t penalize if partial is incorrect.

Screenshots

Test and ResultsThe cmu07a.dic recognized very poorlyhub4_wsj_sc_3s_8k.cd_semi_5000 TOTAL Words: 91

Correct: 56 Errors: 46 TOTAL Percent correct = 61.54%

Error = 50.55% Accuracy = 49.45% TOTAL Insertions: 11 Deletions: 3 Substitutions: 32

hub4_wsj_sc_3s_8k.cd_semi_5000adapt TOTAL Words: 91 Correct: 71 Errors: 25 TOTAL Percent correct = 78.02%

Error = 27.47% Accuracy = 72.53% T TOTAL Insertions: 5 Deletions: 9 Substitutions: 11

Test and ResultsUsing the custom corpus and creating

custom language model the tool accurately detects speech in a timely fashion ~2s.

InstallationNeed to install custom lexical and language

modeling files

Future AdditionsAdapt scoring based on pitch and phoneme

recognition. Add different levels of difficultyShow progress reports

Referenceshttp://developer.android.comhttp://sites.google.com/site/ioSams Teach Yourself Android Application

Development, Lauren Darcey & Shane Conder (2010)