Comprehensive Analysis and Application Practice of Speech Recognition Technology in HarmonyOS Next

This article aims to deeply explore the speech recognition technology in the Huawei HarmonyOS Next system (up to API 12 as of now), and summarize it based on practical development practices. It mainly serves as a vehicle for technical sharing and communication. There may be mistakes and omissions. Colleagues are welcome to put forward valuable opinions and questions so that we can make progress together. This article is original content, and any form of reprint must indicate the source and the original author.

I. Overview of Speech Recognition Technology and Support in HarmonyOS Next

(1) In-depth Explanation of the Basic Principles

In the speech world of HarmonyOS Next, speech recognition technology is like a magical translator, converting the sound signals we speak into text information that computers can understand. Its core principles involve several key steps.

First is audio feature extraction. This step is like extracting key information from the ocean of sound. Common methods use mathematical tools such as the Fourier transform to convert the audio signal in the time domain into a frequency domain signal, and then extract features such as Mel-frequency cepstral coefficients (MFCC). These features can represent the key information of the audio signal, such as frequency and amplitude, providing a basis for subsequent recognition. For example, in a noisy environment, through reasonable feature extraction methods, the features of the speech signal can be highlighted, reducing the interference of environmental noise.

Next is the construction of the acoustic model. The acoustic model is mainly used to describe the relationship between the speech signal and phonemes. In the era of deep learning, commonly used acoustic models are constructed based on convolutional neural networks (CNNs) and recurrent neural networks (RNNs) or their variants (such as LSTM, GRU). These models are trained with a large amount of speech data to learn the acoustic feature representations of speech signals for different phonemes. For example, for the four phonemes with similar pronunciations but different tones in Mandarin Chinese, namely "mā", "má", "mǎ", and "mà", the acoustic model can learn the subtle differences in their acoustic features to accurately distinguish them.

Finally is the application of the language model. The language model is responsible for combining and correcting the phoneme sequence output by the acoustic model according to the context information to generate the final text. Common language models include the statistical n-gram model and the neural network-based language model (such as the Transformer-based language model). The language model can correct the possible errors of the acoustic model according to the grammar, semantic rules of the language, and common vocabulary collocations, etc., to improve the recognition accuracy. For example, when the acoustic model recognizes "我去商店卖东西" (I go to the store to sell things), the language model, based on the context and grammar rules, will determine that the character "卖" (sell) may be wrong, and it is more reasonable to be "买" (buy), thus correcting the recognition result to "我去商店买东西" (I go to the store to buy things).

(2) Introduction to the Characteristics of Speech Recognition Capabilities in HarmonyOS Next

HarmonyOS Next provides a series of powerful characteristics for speech recognition. In terms of language support, it currently mainly supports Mandarin Chinese, which gives it a great advantage in application scenarios in the domestic market and can meet the needs of many Chinese users. For example, in Chinese speech assistant applications, it can accurately recognize various commands issued by users in Mandarin Chinese, such as querying the weather, playing music, setting reminders, etc.

In terms of speech duration, it is divided into a short speech mode and a long speech mode. In the short speech mode, the speech duration does not exceed 60 seconds. This mode is suitable for quick command input or short speech interaction scenarios, such as quickly querying a piece of information or launching an application. The long speech mode supports up to 8 hours of speech recognition, which provides possibilities for some special scenarios, such as the speech-to-text transcription of meeting minutes. In practical applications, choosing the appropriate speech duration mode according to different needs can better exert the effectiveness of speech recognition.

(3) Comparison of the Differences in Application Scenarios of Different Speech Recognition Technologies

In the HarmonyOS Next ecosystem, different speech recognition technologies are suitable for different application scenarios. The speech recognition technology based on traditional template matching has a relatively low computational complexity and does not require high hardware resources. Therefore, it can be used to implement simple speech command recognition on some devices with limited resources (such as low-end smart wearable devices), such as controlling the on/off of the device and switching function modes. However, its recognition accuracy may be affected in complex environments or when there are large changes in speech.

The speech recognition technology based on deep learning has higher recognition accuracy and stronger robustness, and can adapt to the speech recognition requirements in different accents, speaking speeds, intonations, and complex environments. For example, in an intelligent in-vehicle system, drivers may come from different regions with various accents, and the noise in the vehicle is relatively large. The deep learning speech recognition technology can accurately recognize the drivers' speech commands, such as setting the navigation destination and controlling music playback, providing a safer and more convenient driving experience. In an intelligent speech assistant, the deep learning speech recognition technology can understand the natural and fluent speech questions of users and provide more intelligent and personalized answers, which is suitable for various complex interaction scenarios.

II. Implementation of the Speech Recognition Function of the Core Speech Kit

(1) Detailed Elaboration of Interfaces and Classes

The Core Speech Kit provides developers with a rich set of interfaces and classes, enabling them to easily integrate the speech recognition function into HarmonyOS Next applications. Among them, key classes include SpeechRecognizer, etc. It provides a series of methods for operations such as the initialization of speech recognition, parameter setting, starting recognition, and obtaining recognition results.

For example, a speech recognizer instance can be created through the createSpeechRecognizer method, and then the recognition parameters, such as the sampling rate and language type, can be set using the setRecognitionParams method. The design of these interfaces enables developers to flexibly control the speech recognition process and carry out customized development according to the needs of the application.

(2) Code Example and Setting of Recognition Parameters

The following is a simple code example showing how to use the Core Speech Kit to achieve the conversion from speech to text (simplified version):

import { SpeechRecognizer } from '@kit.CoreSpeechKit';

// Create a speech recognizer instance
let recognizer = SpeechRecognizer.createSpeechRecognizer();

// Set recognition parameters
let params = {
    language: 'zh_CN', // Set the language to Mandarin Chinese
    sampleRate: 16000 // Set the sampling rate to 16000Hz (a common audio sampling rate)
};
recognizer.setRecognitionParams(params);

// Start speech recognition
recognizer.startRecognition();

// Register the recognition result callback function
recognizer.on('result', (result) => {
    console.log('Recognition result:', result.text);
});

// Register the recognition end callback function
recognizer.on('end', () => {
    console.log('Recognition ended');
});

In this example, first, a speech recognizer instance is created, and then the recognition language is set to Mandarin Chinese and the sampling rate is set to 16000Hz. Then, the speech recognition is started, and the callback functions for the recognition result and the end of recognition are registered to obtain the result and perform corresponding processing after the recognition is completed.

(3) Analysis of Recognition Accuracy and Performance and Discussion on Optimization

1. Analysis of Factors Affecting Recognition Accuracy In practical applications, the speech recognition accuracy of the Core Speech Kit is affected by various factors. Audio quality is a key factor. If there are problems such as noise interference, too low or too high volume, and incompatible audio encoding format during the audio collection process, the recognition accuracy will be reduced. For example, in a noisy factory environment, if effective noise reduction measures are not taken, the speech recognition system may mistake the environmental noise for the speech signal, resulting in recognition errors.

The factors of the speaker cannot be ignored either. Different accents, speaking speeds, intonations, and pronunciation clarity will all affect the recognition accuracy. For example, users with strong local accents may cause more misrecognition situations in the speech recognition system. In addition, the coverage and accuracy of the language model will also affect the recognition results. If the language model does not include the vocabulary or grammatical structures of certain specific fields, the accuracy may decrease when recognizing speech related to these contents.

1. Discussion on Factors Affecting Performance In terms of performance, the hardware performance of the device has a direct impact on the speech recognition speed. Devices with lower performance may experience delays when processing speech recognition tasks, affecting the user experience. For example, on some low-end smartphones, due to the limited processing power of the CPU, it may take a long time for speech recognition to give results. At the same time, the network condition (if it involves cloud speech recognition services) will also affect the performance. An unstable network connection may lead to data transmission delays or interruptions, making the speech recognition process slow or even fail.
2. Proposed Optimization Methods For the optimization of recognition accuracy, data augmentation techniques can be adopted. Collect more speech data with different accents, speaking speeds, intonations, and in different environments, and train the speech recognition model to improve its adaptability to various speech changes. For example, add some speech samples with dialect accents to the training data so that the model can better recognize speech with different accents. At the same time, continuously update and optimize the language model, expand the vocabulary, and improve the grammatical structure to enhance the accuracy of the language model.

For performance optimization, optimization can be carried out at the audio collection stage. Use high-quality microphones and reasonably set the audio collection parameters, such as automatic gain control, to ensure the stable quality of the collected audio signal. On the device side, the speech recognition model can be optimized, such as using model compression technology to reduce the model size and improve the model loading and inference speed. If it involves cloud speech recognition services, optimize the network communication protocol and use technologies such as data caching and preloading to reduce the impact of network latency on the performance of speech recognition.

III. Expansion of Speech Recognition Applications and Optimization Strategies

(1) Discussion on Expanded Application Scenarios

1. Expansion of Applications in Intelligent Speech Assistants In intelligent speech assistant applications, speech recognition technology is the core of human-computer interaction. In addition to basic functions such as information query and task execution, its functions can be further expanded. For example, combined with the smart home control system, remote control of home devices can be achieved through speech recognition. Users can say commands such as "Turn on the lights in the living room" or "Raise the temperature of the air conditioner in the bedroom". After the speech assistant recognizes the commands, it controls the corresponding devices to perform the operations through the communication interface with the smart home devices. At the same time, use speech recognition technology to implement the voice shopping function. Users can describe the product information by voice, and the speech assistant searches and displays relevant products on the e-commerce platform after recognition, achieving a convenient shopping experience.
2. Expansion of Applications in Intelligent In-vehicle Systems For intelligent in-vehicle systems, the application of speech recognition technology can greatly improve driving safety and convenience. In addition to conventional navigation and music playback control, it can be expanded to vehicle status query and control. For example, the driver can ask the vehicle's remaining fuel quantity, tire pressure, and other information by voice. After the speech recognition system recognizes it, it obtains the data from the vehicle sensors and feeds it back to the driver. It can also realize functions such as voice control of window lifting and seat adjustment, allowing the driver to complete various operations without taking their hands off the steering wheel, reducing the potential safety hazards caused by distracted operations.

(2) Proposed Optimization Strategies

1. Improving Recognition Accuracy through Data Augmentation Data augmentation is one of the effective methods to improve speech recognition accuracy. Various transformation operations can be performed on the original speech data, such as adding noise, changing the speaking speed, and adjusting the pitch, to generate more training data. For example, by adding different types and intensities of environmental noise (such as the noise of a car driving, the noise of a crowd, etc.) to the original speech data, various noise situations in practical application scenarios can be simulated, enabling the speech recognition model to better adapt to noisy environments and improve the recognition accuracy in complex environments. At the same time, perform operations such as random cropping and splicing on the speech data to increase the diversity of the data, allowing the model to learn more different combinations of speech features.
2. Optimizing the Model Structure to Reduce Resource Occupancy In order to adapt to the resource limitations of HarmonyOS Next devices, the speech recognition model structure can be optimized. Adopt lightweight neural network architectures, such as constructing the acoustic model based on the ideas of MobileNet or ShuffleNet, to reduce the number of model parameters and computational complexity. For example, in MobileNet, depthwise separable convolutions are used to replace traditional convolutions, greatly reducing the computational amount while maintaining a certain recognition accuracy. In addition, perform pruning operations on the model to remove unimportant connections or neurons, further reducing the model size. During the pruning process, pay attention to selecting appropriate pruning strategies and thresholds to avoid excessive pruning leading to a decline in recognition performance. After pruning, the model can be fine-tuned to recover part of the performance loss.

(3) Sharing of Development Experience and Precautions

1. Requirements for Audio Collection Quality In speech recognition development, ensuring high-quality audio collection is crucial. Select appropriate microphone devices, and their parameters such as sensitivity and frequency response range should meet the requirements of speech recognition. For example, a microphone used in a noisy environment should have good noise reduction capabilities, which can effectively suppress environmental noise and highlight the speech signal. At the same time, reasonably set the parameters of audio collection, such as the sampling rate and bit depth. Generally, a higher sampling rate (such as 16000Hz or higher) can more accurately capture the details of the speech signal, but it will also increase the data volume and processing burden, and a balance needs to be struck according to the actual situation. In addition, pay attention to the environment of audio collection, avoid collection in spaces with large echoes, and try to reduce external interference factors.
2. Points to Note for Multilingual Support When multilingual support is involved, the characteristics and differences of different languages should be fully considered. Different languages have great differences in speech features, grammatical structures, and vocabulary pronunciations. For example, Chinese is a tonal language, and the change of tones will change the semantics. Special attention should be paid to the processing of tone information during the training and optimization of the speech recognition model. For phenomena such as liaison and weak pronunciation in some languages, they need to be fully reflected in the model training data so that the model can accurately recognize these special speech situations. At the same time, ensure that the training data of the language model and the acoustic model cover various scenarios and usages of multiple languages to improve the accuracy and stability of multilingual recognition. It is hoped that through the introduction of this article, everyone can have a deeper understanding of the speech recognition technology in HarmonyOS Next and can better apply this technology in practical development to provide users with a more intelligent and convenient speech interaction experience. If you encounter other problems in the practice process, you are welcome to communicate and discuss together! Haha!