SPEECH RECOGNITION FOR MOBILE SYSTEMS

BY:PRATIBHA CHANNAMSETTY

SHRUTHI SAMBASIVAN

Introduction• What is speech recognition?

Automatic speech recognition(ASR) is the process by which a computer maps an acoustic speech signal to text.

CLASSIFICATION OF SPEECH RECOGNITION SYSTEM• Users - Speaker dependent system - Speaker independent system -Speaker adaptive system• Vocabulary -small vocabulary : tens of word -medium vocabulary : hundreds of words -large vocabulary : thousands of words -very-large vocabulary : tens of thousands of words.

CLASSIFICATION OF SPEECH RECOGNITION SYSTEM• Word Pattern

- isolated-word system : single words at a time

- continuous speech system : words are connected together

HOW SPEECH RECOGNITION WORKS

APPLICATIONS• Healthcare

• Military

• Helicopters

• Training air traffic controllers

• Telephony and other domains

WHY SPEECH RECOGNITION?• Speech is the easiest and most common way for people to

communicate.• Speech is also faster than typing on a keypad and more expressive than

clicking on a menu item. • Users with low literacy.• Cellphones have widely proliferated the market.

CHALLENGES ON MOBILE DEVICES• Limited available storage space • Cheap and variable microphones • No hardware support for floating point arithmetic • Low processor clock-frequency • Small cache of 8-32 KB• Highly variable and challenging acoustic environments ranging from heavy

background traffic noises to a small room with reverberation of multiple speakers speaking simultaneously • Consume a lot of energy during algorithm execution

ASR MODELS• Embedded speech recognition • Speech recognition in the cloud

• Distributed speech recognition

• Shared speech recognition with user based adaptation(proposed model of use)

EMBEDDED MOBILE SPEECH RECOGNITION

EMBEDDED MOBILE SPEECH RECOGNITION Advantages• Not rely on any communication with a central server

• Cost effective

• Not affected by the latency

EMBEDDED MOBILE SPEECH RECOGNITION Disadvantages

• Cannot perform complex computations

• Lack in terms of speed and memory

• To achieve reliable performance, modifications need to be made to every sub-system of the ASR to take both factors into account.

SPEECH RECOGNITION IN THE CLOUD

SPEECH RECOGNITION IN THE CLOUD Advantages

• Improves speed and accuracy

• It provides an easy way to upgrade or modify the central speech recognition system.

• It can be used for speech recognition with low-end mobile devices such as cheap cellphones.

SPEECH RECOGNITION IN THE CLOUD Disadvantages• Performance degradation

• Acoustic models on the central server need to account for large variations in the different channels.

• Each data transfer over the telephone network can cost money for the end user.

DISTRIBUTED SPEECH RECOGNITION

DISTRIBUTED SPEECH RECOGNITION Advantages

• Does not really need high quality speech

• Improve word error rates

DISTRIBUTED SPEECH RECOGNITION Disadvantages• The major disadvantage of this mode still remains cost and the need of

continuous and reliable cellular connection,.• There’s a need for standardized feature extraction processes that account

for variability's arising due to differences in channel , multi-linguality, variable accents, and gender differences, etc.

SHARED SPEECH RECOGNITION WITH USER BASED ADAPTATION

SHARED SPEECH RECOGNITION WITH USER BASED ADAPTATION Advantages• The ability to function even without network connectivity. • Works well for the limited set of conditions it encounters.• It can be covered successfully by existing mobile devices, if trained or

adapted accordingly. • Server capacity has to be provided only for average, not peak use.

Speech recognition Process in detail

Front-end ProcessInvolves spectral analysis that derives feature vectors to capture salient spectral characteristics of speech input.

Backend ProcessCombines word-level matching and sentence-level search to perform an inverse operation to decode the message from the speech waveform.

Acoustic model• Provides a method of calculating the likelihood of any

feature vector sequence Y given a word W.• Each phone is represented by a HMM.

Language Model• The purpose of the language model is to take advantage of

linguistic constraints to compute the probability of different word sequences • Assuming a sequence of words, ={ 1, 2,…, k}, the 𝐾 𝑊 𝑤 𝑤 𝑤

probability ( ) can be expanded as 𝑃 𝑊𝑃( )=( 1, 2,…, k)𝑊 𝑃𝑤 𝑤 𝑤• We generally make the simplifying assumption that any

word depends only on the previous −1 words in the 𝑤𝑘 𝑁sequence • This is known as an N-gram model • Grammars – Use context free grammars represented by

Finite State Automata (FSA)

Overview of Statistical Speech recognition

Statistical Speech recognition model

• Word sequence is postulated and the language model computes its probability.• Each word is converted into sounds or phones using

pronunciation dictionary.• Each phoneme has a corresponding statistical Hidden

Markov Model (HMM).• HMM of each phoneme is concatenated to form word model

and the likelihood of the data given the word sequence is computed.• This process is repeated for many word sequences and the

best is chosen as the output.

Statistical Speech recognition model

Speech recognition on embedded platforms• Embedded ASR can be deployed either locally or in a

distributed environment with both advantages and disadvantages.

• For LVCSR, embedded devices are limited in terms of CPU power and amount of memory.

• Most importantly, speed is a limiting factor.

Decoding algorithm Asynchronous stack based decoder – memory efficient but complex.Viterbi based decoder – most efficient.3 types of search implementationCombination of static graph and static search spaceStatic graph space with dynamic search spaceDynamic graph

Mobile speech frameworks• Nuance - Dragon mobile SDK• Openears• Sphinx• CeedVocal SDK • Vlingo

Dragon Mobile SDKThe Dragon Mobile SDK provides speech recognition and text-to-speech functionality. The Speech Kit framework provides the classes necessary to perform network-based speech recognition and text-to-speech synthesis. It uses SystemConfiguration and AudioToolbox frameworks.

Speech kit architecture

OpenEars

OpenEars is an iOS framework for iPhone voice recognition and speech synthesis (TTS).It uses the open source CMU Pocketsphinx, CMU Flite, and CMUCLMTK libraries.

OpenEars works by doing the recognition inside the iPhone without using the network.

Sphinx

CMU Sphinx is a open source toolkit for speech recognition developed by Carnegie Melon University.CMU Sphinx is a speaker-independent large vocabulary continuous speech recognizer.

Pocketsphinx — lightweight recognizer library written in C.Sphinx4 — adjustable, modifiable recognizer written in Java.

CeedVocal SDK

CeedVocal SDK is a isolated word speech recognition SDK for iOS. It operates locally on the device and supports 6 languages : English, French, German, Dutch, Spanish and Italian.

Mobile applications using speech recognition• Google now• Siri• S-Voice• Dragon Search• Dragon Dictation• Trippo-Mondo • Verbally

References1. Rethinking Speech Recognition on Mobile Devices, Anuj Kumar, Anuj Tewari, Seth Horrigan, Matthew Kam, Florian Metze and John Canny.2. Towards large vocabulary ASR on embedded platforms, Miroslav Novak.3. Speech Recognition: Statistical Methods, L R Rabiner, B-H Juang.4. http://www.nuancemobiledeveloper.com, 9th April 2013.5. http://cmusphinx.sourceforge.net , 9th April 2013.6. http://www.politepix.com/openears.7. http://www.creaceed.com/ceedvocal