1. CMU Shpinx Speech Recognition Engine Reporter : Chun-Feng Liao NCCU Dept. of Computer Sceince Intelligent Media Lab

2. Purposes of this project Finding out how an efficient speech recognition engine can be implemented. Examine the source code of Sphinx2 to find out the role and function of each component. Reading key chapters of Dr. Mosur K. Ravishankar’s thesis as a reference. Some demo programs will be given during oral presentation.

3. Presentation Agenda Project Summary/ Agenda/ Goal. (In English) Introduction. Basics of Speech Recognitions. Architecture of CMU Sphinx. –Acoustic Model and HMM. –Language Model. Java™ Platform Issues. Demo Conclusion.

4. Voice Technologies In the mid- to late 1990s, personal computers started to become powerful enough to support ASR The two key underlying technologies behind these advances are speech recognition (SR) and text-to-speech synthesis (TTS).

5. Basics of Speech Recognition

6. Speech Recognition Capturing speech (analog) signals Digitizing the sound waves, converting them to basic language units or phonemes( 音素 ). Constructing words from phonemes, and contextually analyzing the words to ensure correct spelling for words that sound alike (such as write and right).

7. Speech Recognition Process Flow Source:Microsoft Speech.NET Home(http://www.microsoft.com/speech/ )http://www.microsoft.com/speech/

8. Recognition Process Flow Summary Step 1:User Input –The system catches user’s voice in the form of analog acoustic signal. Step 2:Digitization –Digitize the analog acoustic signal. Step 3:Phonetic Breakdown –Breaking signals into phonemes.

9. Recognition Process Flow Summary(2) Step 4:Statistical Modeling –Mapping phonemes to their phonetic representation using statistics model. Step 5:Matching –According to grammar, phonetic representation and Dictionary, the system returns an n-best list (I.e.:a word plus a confidence score) –Grammar-the union words or phrases to constraint the range of input or output in the voice application. –Dictionary-the mapping table of phonetic representation and word(EX:thu,thee  the)

10. Architecture of CMU Sphinx.

11. Introduction to CMU Sphinx A speech recognition system developed at Carnegie Mellon University. Consists of a set of libraries –core speech recognition functions –low-level audio capture Continuous speech decoding Speaker-independent

12. Brief History of CMU Sphinx Sphinx-I (1987) –The first user independent,high performance ASR of the world. –Written in C by Kai-Fu Lee ( 李開復博士，現任 Microsoft Asia 首席技術顧問 / 副總裁 ). Sphinx-II (1992) –Written by Xuedong Huang in C. ( 黃學東博士， 現為 Microsoft Speech.NET 團隊領導人 ) –5-state HMM / N-gram LM. ( 我們可以推測， CMU Sphinx 的核心技術對 Microsoft Speech SDK 影響很大。 )

13. Brief History of CMU Sphinx (2) Sphinx 3 (1996) –Built by Eric Thayer and Mosur Ravishankar. –Slower than Sphinx-II but the design is more flexible. Sphinx 4 (Originally Sphinx 3j) –Refactored from Sphinx 3. –Fully implemented in Java. –Not finished yet.

14. Components of CMU Sphinx

15. Front End libsphinx2fe.lib / libsphinx2ad.lib Low-level audio access Continuous Listening and Silence Filtering Front End API overview.API overview

16. Knowledge Base The data that drives the decoder. Three sets of data –Acoustic Model. –Language Model. –Lexicon (Dictionary).

17. Acoustic Model /model/hmm/6k Database of statistical model. Each statistical model represents a phoneme. Acoustic Models are trained by analyzing large amount of speech data.

18. HMM in Acoustic Model HMM represent each unit of speech in the Acoustic Model. Typical HMM use 3-5 states to model a phoneme. Each state of HMM is represented by a set of Gaussian mixture density functions. Sphinx2 default phone set.default phone set

19. Gaussian Mixtures Refer to text book p 33 eq 38 Represent each state in HMM. Each set of Gaussian Mixtures are called “senones”. HMM can share “senones”.

21. Language Model Describes what is likely to be spoken in a particular context Word transitions are defined in terms of transition probabilities Helps to constrain the search space See examples of LM.examples of LM

22. N-gram Language Model Probability of word N dependent on word N-1, N-2,... Bigrams and trigrams most commonly used Used for large vocabulary applications such as dictation Typically trained by very large (millions of words) corpus

23. Decoder Selects next set of likely states Scores incoming features against these states Drop low scoring states Generates results

24. Speech in Java™ Platform

25. Sun Java Speech API First released on October 26, 1998. The Java™ Speech API allows Java applications to incorporate speech technology into their user interfaces. Defines a cross-platform API to support command and control recognizers, dictation systems and speech synthesizers.

26. Implementations of Java Speech API Open Source –FreeTTS / CMU Sphinx4. IBM Speech for Java. Cloud Garden. L&H TTS for Java Speech API. Conversa Web 3.0.

27. Free TTS Fully implemented with Java. Based upon Flite 1.1: a small run- time speech synthesis engine developed at CMU.Flite 1.1 Partial support for JSAPI 1.0. –Speech Recognition functions. –JSML.

28. Sphinx 4 (Sphinx 3j) Fully implemented with Java. Speed is equal or faster than Sphinx3. Acoustic model and Language model is under construction. Source code are available by CVS.(but you can not run any applications without models !) For Example : To check out the Sphinx4,you can using the following command. cvs -z3 -d:pserver:anonymous@cvs.sourceforge.net:/cvsroot/cmusphinx co sphinx4

29. Java™ Platform Issues GC makes managing data much easier Native engines typically optimize inner loops for the CPU – can't do that on the Java platform. Native engines arrange data to optimize cache hits – can't really do that either.

30. DEMO Sphinx-II batch mode. Sphinx-II live mode. Sphinx-II Client / Server mode. A Simple Free TTS Application. (Java-based) TTS vs (c-based)SR. Motion Planner with Free TTS-using Java Web Start™.(This is GRA course final project)

31. Summary Sphinx is a open source Speech Recognition developed at CMU. FE / KB / Decoder form the core of SR system. FE receives and processes speech signal. Knowledge Base provide data for Decoder. Decoder search the states and return the results. Speech Recognition is a challenging problem for the Java platform.

32. Reference Mosur K.Ravishankar, Efficient Alogrithms for Speech Recognition, CMU, 1996. Mosur K.Ravishankar, Kevin A. Lenzo,Sphinx-II User Guide, CMU,2001. Xuedong Huang,Alex Acerd,Hsiao- Wuen hon,Spoken Language Processing,Prentice Hall,2000.

33. Reference (on-line) On-line documents of Java™ Speech API –http://java.sun.com/products/java- media/speech/http://java.sun.com/products/java- media/speech/ On-line documents of Free TTS –http://freetts.sourceforge.net/docs/ On-line documents of Sphinx-II –http://www.speech.cs.cmu.edu/sphinx/

34. Q & A