Which speech models best help with Spanish accent improvement

The best speech models for Spanish accent improvement to help with accent training and intelligibility tend to utilize advanced architectures such as large acoustic pretrained models (e.g., Wav2Vec 2.0, ECAPA-TDNN), multi-task learning frameworks, and synthetic speech data. Key findings from research and experiments include:

• Models like Wav2Vec 2.0 and ECAPA-TDNN have proven effective for accent classification and can be integrated into accent training workflows to improve automatic speech recognition for accented speech, including Spanish accents. ¹
• Multi-task learning (MTL) approaches that jointly model accent-related tasks (such as native-ness detection and speaker recognition) outperform single-task models, particularly when training data is limited. ²
• Synthetic speech data of Spanish-accented English has been used to analyze pronunciation patterns and improve robustness of ASR systems to Spanish accents, helping with phonemic variation modeling but less so for phonotactics. ³
• Generative error-correction models combined with multi-task learning for accent recognition (e.g., MMGER model) refine both speech recognition and accent-specific corrections, further aiding accent improvement. ⁴
• Fine-tuning pretrained ASR models with accent-specific data improves recognition accuracy and provides better feedback for pronunciation training. ⁵
• Recent models exploring detailed phonetic and articulatory representations also improve accent conversion and aid in better accent adaptation in speech synthesis and recognition. ⁶

In summary, deep learning models based on pretrained acoustic features, multi-task learning, synthetic data augmentation, and specific architectures like Wav2Vec 2.0 and ECAPA-TDNN are currently the most promising for helping with Spanish accent improvement in speech applications, whether for accent classification, recognition, or training. ^{1, 2, 3, 4, 6}

References

• CommonAccent: Exploring Large Acoustic Pretrained Models for Accent Classification Based on Common Voice

• Multi-Task Learning for Voice Related Recognition Tasks

• Investigating the Use of Synthetic Speech Data for the Analysis of Spanish-Accented English Pronunciation Patterns in ASR

• MMGER: Multi-Modal and Multi-Granularity Generative Error Correction With LLM for Joint Accent and Speech Recognition

• Improvement in Automatic Speech Recognition of South Asian Accent Using Transfer Learning of DeepSpeech2

• Accent Conversion with Articulatory Representations

• Improvement of Accent Classification Models Through Grad-Transfer From Spectrograms and Gradient-Weighted Class Activation Mapping

• Open-Source Multispeaker Text-to-Speech Model and Synthetic Speech Corpus with a Mexican Accent through a Web Spanish Dictionary

• Mitigating Gender Bias in Accent Recognition Through Gender-Specific Models: An Analysis of Performance and Fairness

• Pitch Accent Detection improves Pretrained Automatic Speech Recognition

• Enhanced Machine Learning Framework for English Speaking Assessment: Addressing Accent and Speech Variability Through BiLSTM-Transformer Architecture

• Pushing the performances of ASR models on English and Spanish accents

• Evidence-Based Design Principles for Spanish Pronunciation Teaching

• Remap, warp and attend: Non-parallel many-to-many accent conversion with Normalizing Flows

• Accented Text-to-Speech Synthesis with Limited Data

• Residual Adapters for Parameter-Efficient ASR Adaptation to Atypical and Accented Speech

• Perceptual learning of systematic variation in Spanish-accented speech.

• Spanish Pre-trained BERT Model and Evaluation Data

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

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

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

• Accented Speech Recognition: A Survey

• MeWEHV: Mel and Wave Embeddings for Human Voice Tasks

• Explicit Intensity Control for Accented Text-to-speech

• Multilingual Multiaccented Multispeaker TTS with RADTTS

• Foreign English Accent Adjustment by Learning Phonetic Patterns