How can speech technology assist in reducing Chinese accents

Speech technology can assist in reducing Chinese accents through several advanced methods such as speech recognition, speech synthesis, accent detection, and accent conversion.

1. Pronunciation Error Detection and Correction: Intelligent speech technology can identify pronunciation errors typical of Chinese accents using speech recognition algorithms and then provide corrective feedback through speech synthesis. This helps learners detect and gradually reduce their accent by mimicking correct pronunciations. ^{1, 2}

2. Accent Conversion Systems: Accent conversion technology can transform speech with a Chinese accent into a more native-like accent while preserving the speaker’s voice identity. These systems use sophisticated generative models that work on semantic representations to convert accented speech into a native-like accent with minimal supervision or data. ^{3, 4, 5, 6}

3. Machine Learning-Based Accent Detection: Speech technology uses machine learning models to classify native versus non-native accents, supporting speech applications in adjusting and adapting to accent variations for better recognition and correction. ^{7, 8}

4. Computer-Assisted Pronunciation Training (CAPT): CAPT systems leverage speech generation and recognition for accent reduction, often using neural network architectures to detect pronunciation errors and guide learners with speech feedback. ⁹

5. Speech Synthesis for Accent Neutralization: Advanced speech synthesis models generate speech with native-like pronunciation. They help learners by providing examples of correct pronunciations and offer customized feedback. ¹⁰

Overall, these speech technologies assist Chinese speakers by detecting accented pronunciations, providing accurate native-like speech models, and enabling personalized, iterative practice that leads to accent reduction and clearer English or other second-language speech. ^{2, 5, 1, 3, 9}

References

• Improving Mandarin Chinese Learning in Tibetan Second-Language Learning by Artificial Intelligent Speech Technology

• CorrectSpeech: A Fully Automated System for Speech Correction and Accent Reduction

• Convert and Speak: Zero-shot Accent Conversion with Minimum Supervision

• TTS-Guided Training for Accent Conversion Without Parallel Data

• Improving Pronunciation and Accent Conversion through Knowledge Distillation And Synthetic Ground-Truth from Native TTS

• Accent conversion using discrete units with parallel data synthesized from controllable accented TTS

• Native and Non-Native English Speech Classification: A premise to Accent Conversion

• Spoken Accent Detection in English Using Audio-Based Transformer Models

• Computer-assisted Pronunciation Training — Speech synthesis is almost all you need

• Lightweight convolution-based Chinese Speech Synthesis Method

• Chinese multi-dialect speech recognition based on instruction tuning

• Exploring Phonetic Differences and Cross-Linguistic Influences: A Comparative Study of English and Mandarin Chinese Pronunciation Patterns

• Integrating international Chinese visualization teaching and vocational skills training: leveraging attention-connectionist temporal classification models

• DLD: An Optimized Chinese Speech Recognition Model Based on Deep Learning

• Hearing Assistive Technology Facilitates Sentence-in-Noise Recognition in Chinese Children With Autism Spectrum Disorder.

• The impact of automatic speech recognition technology on second language pronunciation and speaking skills of EFL learners: a mixed methods investigation

• The impact of automatic speech recognition technology on second language pronunciation and speaking skills of EFL learners: a mixed methods investigation

• AccentBox: Towards High-Fidelity Zero-Shot Accent Generation

• Non-parallel Accent Transfer based on Fine-grained Controllable Accent Modelling

• A Novel Chinese Dialect TTS Frontend with Non-Autoregressive Neural Machine Translation

• Deep Speech 2: End-to-End Speech Recognition in English and Mandarin

• Qifusion-Net: Layer-adapted Stream/Non-stream Model for End-to-End Multi-Accent Speech Recognition

• Voice-preserving Zero-shot Multiple Accent Conversion

• Non-autoregressive real-time Accent Conversion model with voice cloning

• Standardized Evaluation Method of Pronunciation Teaching Based on Deep Learning

• Generating tonal distinctions in Mandarin Chinese using an electrolarynx with preprogrammed tone patterns