3D-Speaker: A Large-Scale Multi-Device, Multi-Distance, and Multi-Dialect Corpus for Speech Representation Disentanglement (2306.15354v3)

Abstract: Disentangling uncorrelated information in speech utterances is a crucial research topic within speech community. Different speech-related tasks focus on extracting distinct speech representations while minimizing the affects of other uncorrelated information. We present a large-scale speech corpus to facilitate the research of speech representation disentanglement. 3D-Speaker contains over 10,000 speakers, each of whom are simultaneously recorded by multiple Devices, locating at different Distances, and some speakers are speaking multiple Dialects. The controlled combinations of multi-dimensional audio data yield a matrix of a diverse blend of speech representation entanglement, thereby motivating intriguing methods to untangle them. The multi-domain nature of 3D-Speaker also makes it a suitable resource to evaluate large universal speech models and experiment methods of out-of-domain learning and self-supervised learning. https://3dspeaker.github.io/

• The paper introduces a novel speech corpus featuring over 10,000 speakers to enable the disentanglement of speaker identity, dialect, and environmental factors.
• It employs multi-device, multi-distance, and multi-dialect recordings, capturing 579,013 utterances over 1,124+ hours to simulate realistic speech conditions.
• Baseline experiments with models like ERes2Net validate the corpus's effectiveness in enhancing speaker verification and robust automatic speech recognition research.

Analysis of "3D-Speaker: A Large-Scale Multi-Device, Multi-Distance, and Multi-Dialect Corpus for Speech Representation Disentanglement"

The paper "3D-Speaker: A Large-Scale Multi-Device, Multi-Distance, and Multi-Dialect Corpus for Speech Representation Disentanglement" presents the development of a substantial speech corpus designed to advance research in disentangling speech representations. Disentanglement is essential in identifying distinct components such as speaker identity, dialect, and environmental factors within speech data.

Corpus Composition and Features

3D-Speaker is a meticulously structured dataset featuring over 10,000 speakers, recorded across multiple devices, distances, and dialects. This corpus comprises:

• Multi-Device Recording: Utilization of varying recording devices like iPads, Android phones, iPhones, PCs, and microphones to provide diverse audio data.
• Multi-Distance Recording: Recordings capture speech at distances ranging from 0.1m to 4m, simulating real-world scenarios.
• Multi-Dialect Representation: Includes speakers using both standard Mandarin and regional dialects, which enhances linguistic diversity.

The dataset boasts over 579,013 utterances with a cumulative duration exceeding 1,124 hours.

Research Implications

The multi-dimensional nature of 3D-Speaker promotes research into various speech processing tasks, such as:

• Speaker Verification (SV): By facilitating the isolation of speaker-specific characteristics from a speech signal, this corpus aids in improving SV systems.
• Speech Recognition (ASR): The dataset's controlled variations enable robust ASR models resilient to device and distance variability.
• Disentangled Representation Learning: Encourages the development of methodologies capable of extracting distinct speech characteristics, minimizing interference from extraneous factors.

Benchmark Performance

The study outlines baseline experiments using ECAPA-TDNN, CAM++, and ERes2Net models, assessing tasks like cross-device, cross-distance, and cross-dialect speaker verification. ERes2Net Large, for instance, achieved notable performance, demonstrating the dataset's suitability for evaluating disentanglement techniques.

Additional Research Avenues

3D-Speaker's rich feature set is conducive to exploring:

• Out-of-Domain Learning: Facilitates testing model adaptability when confronted with unseen devices or dialects during training.
• Self-Supervised Learning: The diversity of the dataset supports innovative approaches to self-supervised learning in acoustic domains.
• Universal Speech Model Evaluation: The versatility inherent in 3D-Speaker allows for the evaluation of large-scale models across various speech-related tasks and scenarios.

Conclusion

The 3D-Speaker corpus stands out due to its scale and diversity, offering an extensive resource for the exploration of speech-related tasks and the development of disentanglement methods. The availability of such a rich dataset can significantly accelerate the progress of speech processing research, offering substantial empirical data for evaluating and refining speech models across various domains.

Future research could leverage this dataset to explore improved model architectures for disentanglement and to explore cross-domain generalization challenges. The dataset also paves the way for advancements in robust speech processing techniques that can better mirror the complexities of real-world audio environments.