Generalized End-to-End Loss for Speaker Verification (1710.10467v5)

Abstract: In this paper, we propose a new loss function called generalized end-to-end (GE2E) loss, which makes the training of speaker verification models more efficient than our previous tuple-based end-to-end (TE2E) loss function. Unlike TE2E, the GE2E loss function updates the network in a way that emphasizes examples that are difficult to verify at each step of the training process. Additionally, the GE2E loss does not require an initial stage of example selection. With these properties, our model with the new loss function decreases speaker verification EER by more than 10%, while reducing the training time by 60% at the same time. We also introduce the MultiReader technique, which allows us to do domain adaptation - training a more accurate model that supports multiple keywords (i.e. "OK Google" and "Hey Google") as well as multiple dialects.

• The paper introduces GE2E loss which achieves a 10% performance boost over TE2E and reduces training time by 60%.
• It leverages a similarity matrix to pull embeddings towards their centroids while pushing apart non-matching ones for robust verification.
• The MultiReader technique adapts the model to varied datasets, yielding about a 30% improvement in equal error rate across tasks.

Generalized End-to-End Loss for Speaker Verification

The paper presents a new loss function, named Generalized End-to-End (GE2E) loss, for enhancing the training efficiency of speaker verification models. The authors compare this new loss function with their previously established Tuple-based End-to-End (TE2E) loss and demonstrate significant improvements in both model performance and training time.

Background and Context

Speaker Verification (SV) involves confirming the identity of a speaker based on previously known utterances. This task can be classified into two categories: Text-Dependent Speaker Verification (TD-SV) and Text-Independent Speaker Verification (TI-SV). In TD-SV, both enroLLMent and verification utterances follow a specific transcript, while TI-SV imposes no lexical constraints. Traditional methods have relied on i-vector based systems; however, recent studies emphasize neural networks and end-to-end training for better accuracy.

Proposed Methodology

Generalized End-to-End Loss

The authors propose the GE2E loss function, which overcomes several limitations of the TE2E loss. Specifically, the key differences and advantages of GE2E include:

1. Batch Processing: GE2E processes a large batch of utterances in one step, which is more efficient than TE2E's tuple-based approach.
2. Similarity Matrix: GE2E constructs a similarity matrix between embedding vectors and centroids, compared to TE2E's scalar similarity value.
3. Emphasis on Difficult Examples: GE2E includes both a softmax implementation and a contrast implementation, focusing on challenging negative samples to enhance the model's robustness.

GE2E leverages a similarity matrix where each element signifies the cosine similarity between an embedding vector and centroids. This matrix facilitates an efficient training process, pushing embeddings closer to their corresponding centroids and away from others.

MultiReader Technique

The paper also introduces the MultiReader technique for domain adaptation, enabling the model to support multiple keywords and dialects. The method combines different data sources, each potentially of varying sizes, such as using both "OK Google" and "Hey Google" keyword datasets.

Experimental Results

The experiments cover both TD-SV and TI-SV tasks, highlighting the efficacy of the proposed GE2E loss and MultiReader technique.

Text-Dependent Speaker Verification

The experiments utilize datasets for multiple keyword support, showing a substantial improvement in Equal Error Rate (EER). The results indicate that using the MultiReader technique provides around 30% relative improvement across various enrolment-verification combinations. Additionally, GE2E achieves a 10% relative improvement over TE2E and reduces training time by 60%.

Text-Independent Speaker Verification

For TI-SV, the authors report a significant decrease in EER when employing GE2E compared to both softmax and TE2E approaches. Their experiments reveal that GE2E lowers EER by more than 10% and that the training process is approximately three times faster.

Implications and Future Directions

The findings show that the GE2E loss function substantially enhances the efficiency and effectiveness of speaker verification models. It achieves lower EERs and faster convergence times, making it well-suited for real-world applications, such as voice-activated assistants that require prompt and accurate speaker verification.

Moreover, the MultiReader technique's ability to leverage multi-domain datasets implies that models can be trained to be more versatile and adaptable. This flexibility is crucial for expanding the applicability of speaker verification systems across different languages and keyword triggers.

Future research could explore expanding the GE2E and MultiReader techniques to other related tasks, such as speaker identification and diarization, to assess their generality and further improve their robustness.

In conclusion, the proposed GE2E loss function and MultiReader technique represent significant steps forward in the domain of speaker verification, offering tangible benefits in model performance and training efficiency.