Speech Recognition
- 3. Speech recognition (also known as automatic speech recognition or computer speech recognition) converts spoken words to text.The term "voice recognition" is sometimes used to refer to speech recognition where the recognition system is trained to a particular speaker.
- 4. Applications of Speech RecognitionSpeech recognition applications include Voice dialing (e.g., "Call home"),
- 5. Call routing (e.g., "I would like to make a collect call"),
- 6. Simple data entry (e.g., entering a credit card number),
- 7. Preparation of structured documents (e.g., A radiology report),
- 8. Speech-to-text processing (e.g., word processors or emails), and
- 9. In aircraft cockpits (usually termed Direct Voice Input).Cont.,Automatic translation; Automotive speech recognition (e.g., Ford Sync); Telematics (e.g. Vehicle Navigation Systems); Court reporting (Realtime Voice Writing); Hands-free computing: voice command recognition computer user interface; Home automation; Interactive voice response; Mobile telephony, including mobile email; Multimodal interaction; Pronunciation evaluation in computer-aided language learning applications; Robotics; Video games, with Tom Clancy's EndWar and Lifeline as working examples; Transcription(digital speech-to-text); Speech-to-text (transcription of speech into mobile text messages); Air Traffic Control Speech Recognition
- 10. Speech recognition techniquesAnalysis techniques are similar for speech and speaker recognition.The following are techniques in SR :Modal evaluationText dependenceStochastic modalsVector quantizationCepstral analysis (High Recognition Accuracy)Orthogonal LPC parametersNeural network approaches
- 11. Speech Recognition ArchitectureThe noisy channel model of individual wordsThe noisy model channel applied to entire sentence
- 12. Speech Recognition ArchitectureThe goal of the probabilistic noisy channel architecture for speech recognition can be summarized as follows : What is the most likely sentence out of all sentences in the language L given some acoustic input O ?
- 13. Speech Recognition ArchitectureThree stage for speech recognition system Signal processing or Feature extraction stage :Waveform is sliced up into frames.Waveform are transformed into spectral features.Subwordor Phone recognition stage :Recognize individual speech. Decoding stage :Find the sequence of words that most probably generated the input
- 14. Overview of HMMsMarkov chains used “to model pronunciation”.Forward algorithm：Phonesequences likelihood.Real input is not symbolic: Spectral featuresInput symbols do not correspond to machine states.Note: Why HMMs are used in speech recognition is that a speech signal could be viewed as a piecewise stationary signal or a short-time stationary signal.
- 15. Speech Recognition RequirementsTo use speech recognition, you need the following:A high quality close-talk (headset) microphone with gain adjustment (gain adjustment: A microphone feature that allows your input to be amplified so that it is made louder for use by the system.) support (A universal serial bus (USB) microphone is recommended.)A 400 megahertz (MHz) or faster computer128 MB or more of memoryWindows 2000 with Service Pack 3 or Windows XP or laterMicrosoft Internet Explorer 5.01 or later
- 17. Automatic Speech Recognition System for Home Appliances Control Abstract - In the present work we study the performance of a speech recognizer for the Greek language, in a smart-home environment. This recognizer operates in spoken interaction scenarios, where the users are able to control various home appliances. In contrast to command and control systems, in our application the users speak spontaneously, beyond the use of a standardized set of isolated commands. The operational performance was tested over various environmental conditions, for two different types of microphones.
- 18. Different Home Appliances Control Scenarios
- 19. Dialogue systemsDialogue systems play a key role in any kind of conversational spoken language interface.Intelligent interfaces of home appliances provide the means for facilitating the operation of these devices, within a dialogue system. Various systems for home appliance control have been reported in the literature, focusing on enhancing the performance of the speech recognition process
- 21. Architecture ExplanationThe audio signal from the user is captured and passed through a speech recognition module that produces a recognition hypothesis. This recognition hypothesis is then forwarded to a language understanding component that creates a corresponding semantic representation. This semantic input is then passed to the dialog manager, which, based on the current input and discourse context, produces the next system action (typically in the form of a semantic output). A language generation module then produces the corresponding surface (textual) form, which is subsequently passed to a speech synthesis module and rendered as audio back to the user.
- 22. Function of dialog managerThe dialog manager therefore plays a key control role in any conversational spoken language interface: given the decoded semantic input corresponding to the current user utterance and the current discourse context, it determines the next system action. In essence, the dialog manager is responsible for planning and maintaining the coherence, over time of the conversation.
- 23. Steps a dialogue manager doFirst, the dialog manager must maintain a history of the discourse and use it to interpret the perceived semantic inputs in the current context. Second, a representation – either explicit or implicit – of the system task is typically required. The current semantic input, together with the current dialog state and information about the task to be performed is then used to determine the next system action.