Speech recognition
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Speech recognition, also known as automatic speech recognition or speech to text, is the ability to translate spoken words into text. The process works by a microphone converting sound waves into a digital format, which software then analyzes to break down the message and output text on a screen. Speech recognition has many uses and while it allows for faster, hands-free input with better spelling, factors like slang, noise interference, and overlapping speech can impact accuracy. Popular speech recognition programs include Dragon, Siri, Microsoft SYNC, and Mac Speech Dictate.
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A speech recognition dataset is a collection of audio recordings and corresponding transcriptions that are used to train and evaluate speech recognition models. These datasets are meticulously curated to include a diverse range of voices, accents, dialects, and speaking styles to ensure that the resulting models are robust and versatile.
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The quality and diversity of a speech recognition dataset directly influence the performance of the speech recognition system. A well-curated dataset enables the development of models that can accurately understand and transcribe speech from a wide array of speakers, including those with different accents or speech impediments.
Moreover, speech recognition datasets are pivotal in advancing research and development in the field. They provide a benchmark for comparing the effectiveness of different algorithms and approaches, fostering innovation and continuous improvement in speech recognition technology.
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Creating a comprehensive speech recognition dataset is not without its challenges. It requires the collection of vast amounts of audio recordings, which must then be accurately transcribed. Ensuring the diversity of the dataset is also crucial, as it must represent various demographics, languages, and speaking conditions (such as noisy environments).
Privacy and consent are additional concerns, as the collection and use of voice recordings must adhere to ethical guidelines and regulations to protect individuals' personal information.
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Speech recognition datasets are the unsung heroes behind the seamless voice interactions we enjoy with our technology today. They are the foundation upon which speech recognition systems are built, enabling them to understand and respond to our spoken commands accurately. As we continue to push the boundaries of what's possible with speech recognition, the role of these datasets will only grow in importance, driving innovation and shaping the future of human-computer interaction.
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Speech recognition
Speech recognition, also known as automatic speech recognition or computer speech recognition, allows computers to understand human voice. It has various applications such as dictation, system control/navigation, and commercial/industrial uses. The process involves converting analog audio of speech into digital format, then using acoustic and language models to analyze the speech and output text. There are two main types: speaker-dependent which requires training a model for each user, and speaker-independent which can recognize any voice without training. Accuracy is improving over time as technology advances.
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Voice input and speech recognition system in tourism/social media
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Artificial Intelligence is composed of two words Artificial and Intelligence, where Artificial defines "man made," and intelligence defines "thinking power", hence AI means "a man-made thinking power.“
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Paper on Speech Recognition
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Speech is one of the essential sources of the conversation between human beings. We as humans speak and listen to each other in human-human interface. People have tried to develop systems that can listen and prepare a speech as persons do so naturally. This paper presents a brief survey on Speech recognition, allow people to compose documents and control their computers with their voice. In other words, the process of enabling a machine (like a computer) to identify and respond to the sounds produced in human speech. ASR can be treated as the independent, computer-driven script of spoken language into readable text in real time. The Speech Recognition system requires careful attention to the following issues: Meaning of various types of speeches, speech representation, feature extraction techniques, speech classifiers, and database and performance evaluation. This paper helps in understanding the technique along with their pros and cons. A comparative study of different technique is done as per stages.
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This document provides an overview of speech recognition technology. It defines key terms like utterances, pronunciation, and grammar. It describes how speech recognition works by explaining the acoustic model, grammar, and recognized text. It also discusses standards, performance measurement, and provides an example of Google Search by Voice.
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The document describes the design and development of a Speech to Sign Language Interpreter System (SSLIS). The system uses Sphinx 3.5 for automatic speech recognition (ASR) to convert speech to text. It then maps the recognized text to American Sign Language (ASL) videos stored in a database for translation and live signing. The system aims to improve accessibility for the deaf by providing free, open-source sign language interpretation software.
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This document discusses advances in automatic speech recognition from audio-only to audio-visual speech recognition. It begins by providing background on early work in speech recognition and limitations of audio-only systems. It then describes the key components of audio-visual speech recognition including the audio component, visual component involving face/feature extraction and image processing, and audio-visual integration. Applications of audio-only systems like Siri and voice search are mentioned. The performance benefits of incorporating visual information to enhance noise robustness are discussed.
Speech Recognition Datasets: A Cornerstone for Innovation
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In the realm of artificial intelligence, speech recognition stands as a transformative technology that has revolutionised the way we interact with our devices. From virtual assistants like Siri and Alexa to voice-controlled home automation systems, speech recognition has become an integral part of our daily lives. At the heart of this technological marvel lies a critical component: the speech recognition dataset.
What is a Speech Recognition Dataset?
A speech recognition dataset is a collection of audio recordings and corresponding transcriptions that are used to train and evaluate speech recognition models. These datasets are meticulously curated to include a diverse range of voices, accents, dialects, and speaking styles to ensure that the resulting models are robust and versatile.
Importance of Speech Recognition Datasets
The quality and diversity of a speech recognition dataset directly influence the performance of the speech recognition system. A well-curated dataset enables the development of models that can accurately understand and transcribe speech from a wide array of speakers, including those with different accents or speech impediments.
Moreover, speech recognition datasets are pivotal in advancing research and development in the field. They provide a benchmark for comparing the effectiveness of different algorithms and approaches, fostering innovation and continuous improvement in speech recognition technology.
Challenges in Creating Speech Recognition Datasets
Creating a comprehensive speech recognition dataset is not without its challenges. It requires the collection of vast amounts of audio recordings, which must then be accurately transcribed. Ensuring the diversity of the dataset is also crucial, as it must represent various demographics, languages, and speaking conditions (such as noisy environments).
Privacy and consent are additional concerns, as the collection and use of voice recordings must adhere to ethical guidelines and regulations to protect individuals' personal information.
Conclusion
Speech recognition datasets are the unsung heroes behind the seamless voice interactions we enjoy with our technology today. They are the foundation upon which speech recognition systems are built, enabling them to understand and respond to our spoken commands accurately. As we continue to push the boundaries of what's possible with speech recognition, the role of these datasets will only grow in importance, driving innovation and shaping the future of human-computer interaction.
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This document discusses speech recognition, text-to-speech, and voice interfaces. It describes speech recognition as allowing a user to speak commands into a device instead of pressing buttons. It then discusses uses of speech recognition for individuals with disabilities, medical transcription, dictation, and accessing menu commands. The document goes on to explain how speech recognition works by converting audio into recognized speech through steps like fast Fourier transforms and matching sounds to entries in a codebook. It also discusses discrete vs continuous speech recognition and the future of assistive technology in schools.
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Speech recognition
- 1. Speech recognition By: Rachel Fernandes and Kaitlyn Bryson
- 2. What is speech recognition? Speech Recognition (SR) is the ability to translate a dictation or spoken word to text. Also known as “automatic speech recognition” (ASR), “computer speech recognition”, or “speech to text” (STT)
- 3. How it works... Spoken communication is delivered via a microphone Sound waves are converted into digital format Software analyzes input and breaks down message Software gives an output based on what it perceives and it will be displayed on a computer screen or phone screen
- 4. Process Here’s another look at how SRS works...
- 5. The many uses of SRS
- 6. pros of speech recognition Faster than “hand-writing” Allows for better spelling, whether it be in text or documents Helpful for people with a mental or physical disability Hands-free capability
- 7. Cons of speech recognition No program is 100% perfect Factors that affect the accuracy of speech recognition are: slang, homonyms, signal-to-noise ratio, and overlapping speech Can be expensive depending on the program For example, MacSpeech Dictate had the lack of a transcription feature for recorded voice dictation
- 8. programs Now let’s take a look at a some of the many SRS programs...
- 9. Dragon Speech Recognition for the PC Speech Recognition for the PC Designed to turn your talk into text faster More accurate than ever before Ability to capture ideas, go through email, search the web & control your PC
- 10. Siri Ability to dictate texts or emails Interact with maps and calendar Ability to ask a question and have Siri google the answer Can understand a variety of languages
- 11. Microsoft SYNC Featured in all the newest Ford vehicles Powered by Microsoft Can tell program to: Search through contacts and make phone calls or texts Get directions Search through music library
- 12. MAC SPEECH Dictate • Developed for Mac OS X • Used the Dragon speech recognition engine • Best known for its speed and accuracy
- 13. thank you! Thank you for watching our slideshare!