Multilingual Phoneme-based Speech Processing: Towards Open-Vocabulary Keyword Spotting and Forced Alignment in Any Language

Core Concepts

Phoneme-based models can achieve strong crosslinguistic generalizability to unseen languages for open-vocabulary keyword spotting and zero-shot forced alignment.

Abstract

The authors present a multilingual speech dataset called IPAPACK, which contains over 1000 hours of speech data across 115 languages with phonemic transcriptions. They then propose CLAP-IPA, a multilingual phoneme-speech contrastive embedding model, and IPA-ALIGNER, a neural forced aligner, both of which can generalize to unseen languages. Key highlights: CLAP-IPA can perform zero-shot open-vocabulary keyword spotting in any language, outperforming text-based models on unseen languages. Alignments between phonemes and speech signals emerge from CLAP-IPA's contrastive training, enabling zero-shot forced alignment in unseen languages. IPA-ALIGNER, a finetuned version of CLAP-IPA, can provide competitive performance on word and phone boundary detection compared to HMM-based forced aligners, even on unseen languages. Phoneme-based modeling enables better knowledge transfer across languages compared to text-based modeling, especially for low-resource languages. The authors discuss the challenges and limitations in scaling up high-quality phonemic transcriptions for the world's languages.

Stats

The IPAPACK dataset contains over 1000 hours of speech data across 115 languages. The VoxCommunis corpus contains 803.84 hours of speech data across 38 languages. The FLEURS-IPA subset contains 779.54 hours of speech data across 77 languages. The MSWC-IPA subset contains 613.44 hours of speech data across 36 languages. The DORECO-IPA subset contains 18.99 hours of speech data across 44 languages.

Quotes

"Despite the seeming diversity, sounds of human speech are highly constrained by the anatomical structure of the human vocal tract, which is universally shared by all humans." "Typological research has also shown that most, if not all, human speech can be represented by around 150 phonemes and diacritics."

Key Insights Distilled From

The taste of IPA

by Jian Zhu,Cha... at arxiv.org 04-03-2024

https://arxiv.org/pdf/2311.08323.pdf

Deeper Inquiries

How can we further improve the quality and coverage of phonemic transcriptions for the world's languages, especially for low-resource and endangered languages?

Improving the quality and coverage of phonemic transcriptions for the world's languages, particularly for low-resource and endangered languages, can be achieved through several strategies: Collaboration with Linguists: Working closely with linguists who specialize in phonetics and phonology can ensure accurate and consistent transcriptions. Linguists can provide expertise in identifying and transcribing phonemes accurately, especially in languages with complex phonetic systems. Crowdsourcing and Community Involvement: Engaging native speakers and language enthusiasts through crowdsourcing platforms can help gather phonemic transcriptions for a wide range of languages. This approach can leverage the collective knowledge of communities to improve coverage. Development of Language-Specific Tools: Creating tools and resources tailored to specific languages can facilitate the transcription process. This includes developing pronunciation dictionaries, phonetic transcription software, and speech recognition tools customized for each language. Integration of Machine Learning: Utilizing machine learning algorithms, such as grapheme-to-phoneme conversion models, can automate and streamline the transcription process. Training models on diverse datasets can improve accuracy and coverage for a variety of languages. Validation and Quality Control: Implementing rigorous validation processes, such as expert review and verification of transcriptions, can ensure the quality and reliability of the phonemic data. Continuous quality control measures are essential for maintaining accuracy. Data Sharing and Collaboration: Encouraging data sharing and collaboration among researchers, institutions, and organizations can expand access to phonemic transcriptions and promote the development of comprehensive datasets for multiple languages. Focus on Endangered Languages: Prioritizing the documentation and preservation of endangered languages is crucial. Specialized efforts and resources should be allocated to collect and transcribe phonemic data for languages at risk of extinction.

How can we make the proposed multilingual speech processing models more computationally efficient for real-world deployment on mobile devices?

To enhance the computational efficiency of multilingual speech processing models for deployment on mobile devices, the following strategies can be implemented: Model Optimization: Employ techniques like model pruning, quantization, and distillation to reduce the size and complexity of the models without compromising performance. This can help in making the models more lightweight and suitable for mobile deployment. Hardware Acceleration: Utilize hardware accelerators like GPUs, TPUs, or specialized AI chips to speed up inference and reduce latency on mobile devices. Optimizing the model architecture to leverage these accelerators can improve efficiency. On-Device Processing: Shift towards on-device processing rather than relying on cloud-based solutions to minimize data transfer and processing delays. This approach can enhance privacy, reduce network dependency, and improve real-time performance. Selective Loading: Implement techniques to load only necessary components of the model during inference, reducing memory usage and speeding up processing. This can involve dynamic loading of model segments based on the input data. Quantized Inference: Utilize quantization techniques to convert model weights and activations to lower precision formats, reducing memory requirements and computational complexity during inference on mobile devices. Efficient Algorithms: Explore the use of efficient algorithms and architectures specifically designed for mobile deployment, such as lightweight transformer variants, efficient attention mechanisms, and optimized neural network structures. Model Parallelism: Implement model parallelism techniques to distribute computations across multiple cores or threads on mobile devices, enabling parallel processing and faster inference times. By incorporating these strategies, multilingual speech processing models can be optimized for efficient deployment on mobile devices, ensuring scalability, speed, and resource efficiency in real-world applications.

What other speech processing tasks beyond keyword spotting and forced alignment can benefit from the crosslinguistic generalization enabled by phoneme-based modeling?

Several speech processing tasks can benefit from the crosslinguistic generalization enabled by phoneme-based modeling, including: Speech Recognition: Phoneme-based modeling can improve speech recognition systems by providing a universal representation of speech sounds across languages. This can enhance the accuracy and robustness of speech-to-text conversion in multilingual settings. Speaker Diarization: Phoneme-based models can aid in speaker diarization tasks by enabling more accurate segmentation and identification of speakers in audio recordings. The universal phonetic representations can improve speaker clustering and tracking across languages. Language Identification: Phoneme-based modeling can enhance language identification systems by capturing language-specific phonetic patterns. This can improve the accuracy of automatically detecting the language spoken in audio data, especially in code-switching scenarios. Accent Recognition: Phoneme-based models can be utilized for accent recognition tasks by analyzing phonetic variations in speech. This can help identify regional accents, dialects, and speech patterns across different languages. Emotion Recognition: Phoneme-based modeling can contribute to emotion recognition in speech by capturing subtle phonetic cues related to emotions. Universal phonetic representations can aid in detecting emotional states in spoken language, transcending language barriers. Speech Synthesis: Phoneme-based models can enhance text-to-speech synthesis systems by providing a phonetic foundation for generating natural-sounding speech in multiple languages. This can improve the quality and intelligibility of synthesized speech across diverse linguistic contexts. Speech Translation: Phoneme-based modeling can facilitate speech translation tasks by aligning phonetic sequences between source and target languages. This can enable more accurate and context-aware translation of spoken language across different linguistic backgrounds. By leveraging phoneme-based modeling for a wide range of speech processing tasks, researchers and developers can achieve greater crosslinguistic generalization, robustness, and performance in multilingual speech applications.