Speech recognition
- 1. Speech Recognition BY Charu joshi
- 2. Introduction What is Speech Recognition? also known as automatic speech recognition or computer speech recognition which means understanding voice by the computer and performing any required task.
- 3. Where can it be used? - Dictation - System control/navigation - Commercial/Industrial applications - Voice dialing
- 4. Recognition Voice Input Analog to Digital Acoustic Model Language Model Display Speech Engine Feedback
- 5. Acoustic Model An acoustic model is created by taking audio recordings of speech, and their text transcriptions, and using software to create statistical representations of the sounds that make up each word. It is used by a speech recognition engine to recognize speech . Language Model Language modeling is used in many natural language processing applications such as speech recognition tries to capture the properties of a language, and to predict the next word in a speech sequence .
- 6. TYPES OF VOICE RECOGNITION There are two types of speech recognition. One is called speaker-dependent and the other is speaker -independent . Speaker-dependent software is commonly used for dictation software, while speaker-independent software is more commonly found in telephone applications. Speaker-dependent software works by learning the unique characteristics of a single person’s voice, in a way similar to voice recognition. New users must first “train” the software by speaking to it, so the computer can analyze how the person talks. This often means users have to read a few pages of text to the computer before they can use the speech recognition software.
- 7. TYPES OF VOICE RECOGNITION Speaker-independent software is designed to recognize anyone’s voice, so no training is involved. This means it is the only real option for applications such as interactive voice response systems — where businesses can’t ask callers to read pages of text before using the system. The downside is that speaker-independent software is generally less accurate than speaker-dependent software. Speech recognition engines that are speaker independent generally deal with this fact by limiting the grammars they use. By using a smaller list of recognized words, the speech engine is more likely to correctly recognize what a speaker said.
- 8. How do humans do it? Articulation produces sound waves which the ear conveys to the brain for processing
- 9. How might computers do it? Digitization Acoustic analysis of the speech signal Linguistic interpretation Acoustic waveform Acoustic signal Speech recognition
- 11. DIFFERENT PROCESSES INVOLVED Digitization Converting analogue signal into digital representation Signal processing Separating speech from background noise Phonetics Variability in human speech Phonology Recognizing individual sound distinctions (similar phonemes) is the systematic use of sound to encode meaning in any spoken human language Lexicology and syntax Lexicology is that part of linguistics which studies words , their nature and meaning, words' elements, relations between words , words groups and the whole lexicon. .
- 12. DIFFERENT PROCESSES INVOLVED(CONTD.) Syntax and pragmatics Semantics tells about the meaning Pragmatics is concerned with bridging the explanatory gap between sentence meaning and speaker's meaning
- 13. Digitization Analogue to digital conversion Sampling and quantizing Sampling is converting a continuous signal into a discrete signal Quantizing is the process of approximating a continuous range of values Use filters to measure energy levels for various points on the frequency spectrum Knowing the relative importance of different frequency bands (for speech) makes this process more efficient E.g. high frequency sounds are less informative, so can be sampled using a broader bandwidth (log scale)
- 14. Separating speech from background noise Noise cancelling microphones Two mics, one facing speaker, the other facing away Ambient noise is roughly same for both mics Knowing which bits of the signal relate to speech
- 15. EVOLUTION OF VOICE RECOGNITION Pattern Matching Interactive Voice Recognition (IVR ) Dictation Speech Integration into Applications Hands Free – Eyes Free
- 16. Process of speech recognition Speaker Recognition Speech Recognition parsing and arbitration S1 S2 SK SN
- 17. Speaker Recognition Speech Recognition parsing and arbitration Switch on Channel 9 S1 S2 SK SN
- 18. Speaker Recognition Speech Recognition parsing and arbitration Who is speaking? Annie David Cathy S1 S2 SK SN “ Authentication”
- 19. Speaker Recognition Speech Recognition parsing and arbitration What is he saying? On,Off,TV Fridge,Door S1 S2 SK SN “ Understanding”
- 20. Speaker Recognition Speech Recognition parsing and arbitration What is he talking about? Channel->TV Dim->Lamp On->TV,Lamp S1 S2 SK SN “ Switch”,”to”,”channel”,”nine” “ Inferring and execution”
- 21. Framework of Voice Recognition Face Recognition Gesture Recognition parsing and arbitration S1 S2 SK SN “ Authentication” “ Understanding” “ Inferring and execution”
- 22. Speaker Recognition Definition It is the method of recognizing a person based on his voice It is one of the forms of biometric identification Depends of speaker specific characteristics.
- 23. Generic Speaker Recognition System Preprocessing Feature Extraction Pattern Matching Preprocessing Feature Extraction Speaker Model Speech signal Analysis Frames Feature Vector Score
- 24. ADVANTAGES Advantages People with disabilities Organizations - Increases productivity, reduces costs and errors. Lower operational Costs Advances in technology will allow consumers and businesses to implement speech recognition systems at a relatively low cost. Cell-phone users can dial pre-programmed numbers by voice command. Users can trade stocks through a voice-activated trading system. Speech recognition technology can also replace touch-tone dialing resulting in the ability to target customers that speak different languages
- 25. DISADVANTAGES Difficult to build a perfect system. Conversations Involves more than just words (non-verbal communication; stutters etc. Every human being has differences such as their voice, mouth, and speaking style. Filtering background noise is a task that can even be difficult for humans to accomplish.
- 26. Future of Speech Recognition Accuracy will become better and better. Dictation speech recognition will gradually become accepted. Small hand-held writing tablets for computer speech recognition dictation and data entry will be developed, as faster processors and more memory become available. Greater use will be made of "intelligent systems" which will attempt to guess what the speaker intended to say, rather than what was actually said, as people often misspeak and make unintentional mistakes. Microphone and sound systems will be designed to adapt more quickly to changing background noise levels, different environments, with better recognition of extraneous material to be discarded.