Speech recognition technology in C++

Speech recognition technology is a technology that can convert human language into text information that can be recognized by computers. With the development of science and technology, especially the increasing maturity of artificial intelligence technology, speech recognition technology is increasingly used in daily life. C is a popular programming language, and C can also be used to develop speech recognition systems. This article will introduce speech recognition technology in C.

1. The basis of speech recognition

Speech recognition technology usually consists of the following parts::

1. Signal preprocessing: convert the audio signal after preprocessing As a form that is easier to analyze, common processing methods include: noise reduction, speech segmentation, volume normalization, etc.

2. Feature extraction: Extract features from the audio signal to facilitate subsequent classification and identification. Common features include: Mel frequency cepstral coefficient (MFCC), linear predictive coding (LPC), etc.

3. Speech recognition model: Speech recognition models are mainly divided into two categories: statistical-based models and neural network-based models. Common statistics-based methods are Hidden Markov Models (HMM), while neural network-based methods include Deep Neural Networks (DNN) and Recurrent Neural Networks (RNN).

4. Model training: Use the labeled audio data set for training to improve the accuracy of the speech recognition model.

2. Speech recognition in C

As an efficient programming language, C has a wide range of applications in the fields of computer vision and natural language processing. In terms of speech recognition, C also has excellent libraries and toolkits. Jieba word segmentation is a very famous Chinese word segmentation library.

The following will introduce commonly used speech recognition libraries and toolkits in C:

1. CMU Sphinx

CMU Sphinx is developed by Carnegie Mellon University An open source speech recognition toolkit, which includes multiple sub-projects, such as PocketSphinx, SphinxTrain, Sphinx4, etc., among which PocketSphinx is one of the most commonly used sub-projects. It is fast, accurate and flexible, and can run on embedded devices. Suitable for embedded speech recognition applications. SphinxTrain is a toolkit for training and optimizing speech recognition models, while Sphinx4 provides a Java speech recognition library that can be easily used in Java applications.

2. Kaldi

Kaldi is an open source speech recognition toolkit developed by Johns Hopkins University. It includes a variety of speech recognition technologies, including HMM, DNN and RNN, etc. It also supports multiple languages, such as Chinese, Arabic, English, etc. Kaldi also provides some training scripts and models to facilitate user training and optimization.

3. HTK

HTK (Hidden Markov Model Toolkit) is a commonly used speech recognition toolkit developed by Cambridge University. It is based on the HMM model and is used in the field of speech recognition. widely. HTK provides a variety of front-end and back-end processing tools, such as feature extraction, Euclidean distance calculation, Viterbi decoding, etc.

3. Application of Speech Recognition

Speech recognition technology has been widely used, such as smart home, smart transportation, medicine, finance, education and other fields. The following will introduce two application scenarios of speech recognition technology:

1. Voice assistant

Voice assistants have become an indispensable part of people’s daily lives, such as Apple’s Siri, Microsoft’s Cortana, Baidu's DuerOS, etc., can complete some operations through voice commands, such as playing music, sending messages, checking the weather, querying information, etc. The realization of voice assistants is inseparable from speech recognition technology and natural language processing technology.

2. Voice Translation

Voice translation technology can convert speech in one language into text in another language. Google Translate, for example, uses speech recognition technology to convert a spoken language into text and machine translation technology to translate it into another language. Speech translation technology can improve the efficiency and convenience of cross-language communication and has broad application prospects.

Conclusion

Speech recognition technology is an important and constantly developing technology, and C, as a popular programming language, is also widely used in speech recognition applications. By introducing commonly used speech recognition libraries and toolkits in C, we can see the diversity and breadth of speech recognition technology. In the future, as AI technology continues to develop, speech recognition technology will also be more widely used.

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