TristouNet: Triplet Loss for Speaker Turn Embedding (1609.04301v3)

Abstract: TristouNet is a neural network architecture based on Long Short-Term Memory recurrent networks, meant to project speech sequences into a fixed-dimensional euclidean space. Thanks to the triplet loss paradigm used for training, the resulting sequence embeddings can be compared directly with the euclidean distance, for speaker comparison purposes. Experiments on short (between 500ms and 5s) speech turn comparison and speaker change detection show that TristouNet brings significant improvements over the current state-of-the-art techniques for both tasks.

TRISTouNET: Triplet Loss for Speaker Turn Embedding

The paper presents TristouNet, a neural network architecture utilizing Long Short-Term Memory (LSTM) networks combined with the triplet loss paradigm for generating speaker turn embeddings. TristouNet aims to enhance tasks related to speaker verification, speaker identification, and speaker diarization by projecting speech sequences into a fixed-dimensional Euclidean space. The primary objective is to enable straightforward comparison of these sequence embeddings using Euclidean distance, thus improving upon existing methods in speaker comparison and change detection.

Methodology and Architecture

TristouNet employs bi-directional LSTM networks tailored towards sequence modeling, with their outputs subjected to average pooling followed by concatenation and subsequent processing through fully connected layers. Furthermore, the embeddings are constrained to reside on a unit hypersphere. This mechanism is designed to address the representation function $f$ while adopting Euclidean distance $d$ as the comparison function, aligning with the ideal property for speaker embedding as outlined by the authors.

Comparative Analysis and Experiments

Through rigorous experimentation, the authors demonstrate that TristouNet notably surpasses traditional techniques such as BIC and Gaussian divergence in both "same/different" classification tasks and speaker change detection. This improvement is attributed to the triplet loss, which actively discriminates between sequences from the same speaker versus those from different speakers. Notably, an absolute reduction of 6.1% in Equal Error Rate (EER) for sequence durations is achieved, indicating substantial advancements over existing methodologies.

The experiments are based on the ETAPE TV subset dataset, offering a practical evaluation framework. Performance metrics, including EER variations and purity versus coverage for speaker change detection, showcase TristouNet's efficacy across different speech sequence durations, reinforcing its utility in diverse audio processing scenarios.

Theoretical and Practical Implications

The research contributes significant insights into optimizing representation functions for speaker-turn embeddings through neural networks. It offers a promising direction for further exploration in variable-length sequences and their application to broader speaker recognition systems. The framework outlined in TristouNet provides valuable potential for integration into comprehensive speaker diarization processes, contributing to both theoretical understanding and practical improvements.

Future Directions

The paper identifies several avenues for future research, including experimenting with deeper architectures and adapting alternative loss functions such as center loss for enhanced performance. These explorations could yield further refinements in speaker comparison tasks. The authors also underscore the importance of evaluating the holistic impact on full-fledged speaker diarization systems, paving the way for enhanced computational audio processing methodologies.

In summary, TristouNet represents a significant stride in speaker turn embedding through its innovative use of triplet loss and sequence modelling with LSTM networks. Both the numerical results and the structural insights provided suggest further developments in AI-driven audio recognition could find a solid foundation in this research.