Artificial intelligence Speech recognition system
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Speech recognition, also known as automatic speech recognition, allows a computer to understand human voice and perform tasks. It uses acoustic and language models to recognize speech. Acoustic models are statistical representations of sounds created from audio recordings and transcriptions, while language models predict word sequences. There are two main types: speaker-dependent systems require user training to recognize individual voices more accurately, while speaker-independent systems used in applications like phones do not require training but are generally less accurate. The speech recognition process involves digitizing speech, analyzing acoustic signals, and linguistically interpreting the speech to recognize words.
Artificial intelligence Speech recognition system
- 2. 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? System control/navigation e.g. GPS-connected digital maps: “How far is it to the motorway junction?” Commercial/Industrial applications in-car steering systems Voice dialing hands-free use of mobile in car e.g. “Dial office”
- 4. Voice Input Analog to Digital Acoustic Model Language Model Feedback Display Speech Engine
- 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 model 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. Two types of speech recognition. Speaker-Dependent is commonly used for dictation software Speaker-Independent is more commonly found in telephone applications.
- 7. 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.
- 8. 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.
- 9. Articulation produces sound waves which the ear conveys to the brain for processing
- 10. Acoustic waveform Acoustic signal Digitization Acoustic analysis of the Speech recognition speech signal Linguistic interpretation
- 12. • 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 • 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
- 13. Semantics and pragmatics Semantics tells about the meaning Pragmatics is concerned with bridging the explanatory gap between sentence meaning and speaker's meaning Lexicology and syntax Lexicology is that part of linguistics which studies words, their nature, and meaning. Syntax tell about the arrangement of words and phrases to create well-formed sentences.
- 14. S1 Speaker Speech parsing S2 and Recognition Recognition arbitration SK SN
- 15. Switch on S1 Channel 9 Speaker Speech parsing S2 and Recognition Recognition arbitration SK SN
- 16. Who is S1 speaking? Speaker Speech parsing S2 and Recognition Recognition arbitration SK Annie David SN Cathy “Authentication”
- 17. What is he S1 saying? Speaker Speech parsing S2 and Recognition Recognition arbitration SK On,Off,TV SN Fridge,Door “Understanding”
- 18. What is he talking S1 about? Speaker Speech parsing S2 and Recognition Recognition arbitration SK “Switch”,”to”,”channel”,”nine” Channel->TV SN Dim->Lamp On->TV,Lamp “Inferring and execution”
- 19. http://www.slideshare.net/richiebmthimmaia h/automatic-speech-recognition-4721204 http://www.scribd.com/doc/36605017/Voice -Recognition-System-PPT http://www.google.com.pk/url? sa=t&rct=j&q=speech%20recognization %20system%20.ppt http://www.wikipedia.com