What is Automatic Speech Recognition (Speech Recognition) Technology?

Speech Recognition is a technology that helps computers automatically identify and convert spoken language into text. This technology uses methods in computer science and linguistics to understand and process speech. Speech recognition systems can be speaker-dependent, requiring individual training for each person, or speaker-independent, capable of recognizing the speech of anyone without training. This technology is increasingly being applied widely in virtual assistants, security systems, and many other fields.

Speech Recognition is a technology that helps computers automatically identify and convert spoken language into text.

Components of a Speech Recognition System

A speech recognition system operates through the coordination of several components below, which help process and convert speech into text effectively.

• Audio preprocessing: This is the process of processing raw audio signals to improve speech quality, remove noise and unwanted sounds, which helps increase accuracy in recognition.
• Feature extraction: The converted audio signal will be processed into a more understandable and valuable representation, making it easier for the machine learning system to process and analyze.
• Language model weighting: Next is assigning weights to words and phrases to increase the likelihood of accurately recognizing common and contextually relevant words.
• Acoustic modeling: This component analyzes and distinguishes the sound units within the speech signal, helping the system recognize different phonemes, tones, and speaking styles.
• Speaker labeling: Speaker labeling identifies and distinguishes the identities of speakers in a recording, so the system can recognize who is speaking at any given time.
• Profanity filtering: This process aims to filter out inappropriate words, helping to ensure that vulgar or unwanted words do not appear in the recognition results.

A Speech Recognition system operates through the coordination of several components.

Popular Speech Recognition Algorithms

In a speech recognition system, to help accurately convert spoken language into text, the system operates according to algorithms. Below are some common algorithms in Speech Recognition:

• Hidden Markov Models (HMMs): HMMs are statistical models used to simulate the relationship between acoustic features and the dynamics of speech signals over time. HMMs are often applied in traditional speech recognition systems to improve accuracy.
• Natural Language Processing (NLP): NLP is a subfield of artificial intelligence that helps speech recognition systems understand and process spoken language. Key tasks include estimating the probability of word sequences, converting spoken language into standard text, and mapping phonetic units to vocabulary.
• Speaker Diarization (SD): This algorithm classifies speakers in a conversation, assigning speech segments to the corresponding speaker. Speaker Diarization helps identify and distinguish individuals in a dialogue.
• Dynamic Time Warping (DTW): DTW searches for the optimal alignment between two audio sequences, helping to recognize speech more accurately by comparing audio signal sequences over time.
• Deep neural networks: Deep neural networks simulate the human auditory perception process, helping to process and transform audio data to improve accuracy in speech recognition.
• Connectionist Temporal Classification (CTC): CTC helps the speech recognition system perform end-to-end recognition by finding the relationship between audio frames and the output text, which is particularly useful for sequence labeling tasks.

Algorithms in Speech Recognition help convert speech into text.

Applications of Speech Recognition

Speech recognition technology is now widely applied in many fields, bringing significant benefits to both businesses and consumers.

• Automotive Speech recognition systems help enhance driving safety. Drivers use voice commands to control navigation systems or search the radio without taking their hands off the steering wheel.

Drivers can use voice commands to control navigation systems or search the radio.

• Technology Virtual assistants like Google Assistant, Siri, Alexa, and Cortana are being commonly used in people's daily lives. Users can give voice commands to perform basic tasks such as searching for information, playing music, and controlling smart devices, promoting the development of the "Internet of Things."

Virtual assistants bring many benefits to people's daily lives.

• Healthcare Doctors and nurses use speech recognition technology to record diagnoses and treatment notes for patients, saving time and improving work efficiency.

Doctors and nurses use speech recognition technology for necessary tasks.

• Sales In the sales field, speech recognition is used in call centers to record and analyze conversations between customers and staff, improving service quality. At the same time, AI chatbots also help resolve customer requests through automated conversations.

In sales, Speech Recognition systems are used to record and analyze calls.

• Security Voice authentication is a new security method that enhances the level of security in online transactions or access to security systems, thanks to the unique characteristics of each person's voice.

Speech Recognition enhances the level of security in security systems.

Conclusion: Thus, speech recognition technology has the ability to convert speech into text, saving time, improving work efficiency, and enhancing user experience. Viettel AI has developed Speech to Text technology, an application of Speech Recognition, to provide a fast and accurate speech-to-text conversion solution.[1] [2] Contact Viettel AI now for more detailed consultation.

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