Can speech recognition tools help refine your French pronunciation

Speech recognition tools, especially those incorporating automatic speech recognition (ASR) technology, can effectively help refine votre prononciation française. These tools provide immediate and personalized feedback, which enhances pronunciation learning by increasing learner motivation, engagement, and confidence. Research shows that French learners using speech recognition tools experience better pronunciation accuracy in segmental features (like vowels) and suprasegmental aspects (like liaison). Tools like Google Translate’s speech features and other AI-powered systems have demonstrated statistically significant improvements in learners’ French pronunciation through self-regulated practice and mobile-assisted learning environments. These technologies allow for increased access to input, multiple opportunities for speech output, and support learner autonomy, which ultimately helps refine French pronunciation. ^{1, 2, 3, 4, 5, 6}

References

• Exploring the role of digital technology in enhancing culinary students’ pronunciation of French terms

• Mobilizing Instruction in a Second-Language Context: Learners’ Perceptions of Two Speech Technologies

• I Can Speak: improving English pronunciation through automatic speech recognition-based language learning systems

• Using Google Translate’s Speech Features for Self-Regulated French Pronunciation Practice

• Assessing the Pedagogical Potential of Google Translate’s Speech Capabilities: Focus on French Pronunciation

• Mobile speech recognition software: A tool for teaching second language pronunciation

• Extracting Linguistic Knowledge from Speech: A Study of Stop Realization in 5 Romance Languages

• L’impact de l’utilisation de la reconnaissance vocale dans l’enseignement du FLE à l’oral dans les classes du secondaire en Algérie

• EFFECTS OF WORD FREQUENCY AND LENGTH IN ASR AND HUMAN TRANSCRIPTION ACCURACY OF L2 SPEECH

• Automatic language identity tagging on word and sentence-level in multilingual text sources: a case-study on Luxembourgish

• Using AI-Powered Speech Recognition Technology to Improve English Pronunciation and Speaking Skills

• LeBenchmark 2.0: a Standardized, Replicable and Enhanced Framework for Self-supervised Representations of French Speech

• Flowchase: a Mobile Application for Pronunciation Training

• Dyn-ASR: Compact, Multilingual Speech Recognition via Spoken Language and Accent Identification

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

• Pronunciation Dictionary-Free Multilingual Speech Synthesis by Combining Unsupervised and Supervised Phonetic Representations

• DDSupport: Language Learning Support System that Displays Differences and Distances from Model Speech

• Impacts of multicollinearity on CAPT modalities: An heterogeneous machine learning framework for computer-assisted French phoneme pronunciation training

• The IMS Toucan System for the Blizzard Challenge 2023

• Data-Driven Mispronunciation Pattern Discovery for Robust Speech Recognition

• Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages

• An Approach to Mispronunciation Detection and Diagnosis with Acoustic, Phonetic and Linguistic (APL) Embeddings

• The FruitShell French synthesis system at the Blizzard 2023 Challenge

• Potential of ASR for the study of L2 learner corpora

• Automatic Spoken Language Identification using a Time-Delay Neural Network

• MKELM based multi-classification model for foreign accent identification