SpeechRecognitionPresentation

Report
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 Process
Involves spectral analysis that derives feature vectors to capture salient
spectral characteristics of speech input.
Backend Process
Combines 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 probability ()
can be expanded as
()=(1,2,…,k)
• We generally make the simplifying assumption that any word  depends
only on the previous −1 words in the sequence
• This is known as an N-gram model
• Grammars – Use context free grammars represented by Finite State
Automata (FSA)
Statistical Speech recognition model
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.
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 implementation
Combination of static graph and static search space
Static graph space with dynamic search space
Dynamic graph
Mobile speech frameworks
•
•
•
•
•
Nuance - Dragon mobile SDK
Openears
Sphinx
CeedVocal SDK
Vlingo
Dragon Mobile SDK
The 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
References
1. 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

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