1. SPEECH RECOGNITION FOR MOBILE SYSTEMS BY: PRATIBHA CHANNAMSETTY SHRUTHI SAMBASIVAN

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

3. 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.

4. CLASSIFICATION OF SPEECH RECOGNITION SYSTEM Word Pattern - isolated-word system : single words at a time - continuous speech system : words are connected together

5. HOW SPEECH RECOGNITION WORKS

6. APPLICATIONS Healthcare Military Helicopters Training air traffic controllers Telephony and other domains

7. 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.

8. 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

9. ASR MODELS Embedded speech recognition Speech recognition in the cloud Distributed speech recognition Shared speech recognition with user based adaptation(proposed model of use)

10. EMBEDDED MOBILE SPEECH RECOGNITION

11. Advantages Not rely on any communication with a central server Cost effective Not affected by the latency

12. 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.

13. SPEECH RECOGNITION IN THE CLOUD

14. 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.

15. 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.

16. DISTRIBUTED SPEECH RECOGNITION

17. Advantages Does not really need high quality speech Improve word error rates

18. 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.

19. SHARED SPEECH RECOGNITION WITH USER BASED ADAPTATION

20. 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.

21. Speech recognition Process in detail

22. 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.

23. 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.

24. 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)

25. Overview of Statistical Speech recognition Statistical Speech recognition model

26. 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

27. 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.

28. 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

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

30. 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.

31. Speech kit architecture

32. 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.

33. 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.

34. 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.

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

36. 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, 9 th April 2013. 5. http://cmusphinx.sourceforge.net, 9 th April 2013. 6. http://www.politepix.com/openears. 7. http://www.creaceed.com/ceedvocal