ExPO: Explainable Phonetic Trait-Oriented Network for Speaker Verification
- URL: http://arxiv.org/abs/2501.05729v2
- Date: Tue, 14 Jan 2025 07:28:10 GMT
- Title: ExPO: Explainable Phonetic Trait-Oriented Network for Speaker Verification
- Authors: Yi Ma, Shuai Wang, Tianchi Liu, Haizhou Li,
- Abstract summary: We use computational method to verify if an utterance matches the identity of an enrolled speaker.
Despite much success, we have yet to develop a speaker verification system that offers explainable results.
A novel approach, Explainable Phonetic Trait-Oriented (ExPO) network, is proposed in this paper to introduce the speaker's phonetic trait.
- Score: 48.98768967435808
- License:
- Abstract: In speaker verification, we use computational method to verify if an utterance matches the identity of an enrolled speaker. This task is similar to the manual task of forensic voice comparison, where linguistic analysis is combined with auditory measurements to compare and evaluate voice samples. Despite much success, we have yet to develop a speaker verification system that offers explainable results comparable to those from manual forensic voice comparison. A novel approach, Explainable Phonetic Trait-Oriented (ExPO) network, is proposed in this paper to introduce the speaker's phonetic trait which describes the speaker's characteristics at the phonetic level, resembling what forensic comparison does. ExPO not only generates utterance-level speaker embeddings but also allows for fine-grained analysis and visualization of phonetic traits, offering an explainable speaker verification process. Furthermore, we investigate phonetic traits from within-speaker and between-speaker variation perspectives to determine which trait is most effective for speaker verification, marking an important step towards explainable speaker verification. Our code is available at https://github.com/mmmmayi/ExPO.
Related papers
- Character-aware audio-visual subtitling in context [58.95580154761008]
This paper presents an improved framework for character-aware audio-visual subtitling in TV shows.
Our approach integrates speech recognition, speaker diarisation, and character recognition, utilising both audio and visual cues.
We validate the method on a dataset with 12 TV shows, demonstrating superior performance in speaker diarisation and character recognition accuracy compared to existing approaches.
arXiv Detail & Related papers (2024-10-14T20:27:34Z) - Can Authorship Attribution Models Distinguish Speakers in Speech Transcripts? [4.148732457277201]
Authorship verification is the task of determining if two distinct writing samples share the same author.
In this paper, we explore the attribution of transcribed speech, which poses novel challenges.
We propose a new benchmark for speaker attribution focused on human-transcribed conversational speech transcripts.
arXiv Detail & Related papers (2023-11-13T18:54:17Z) - Improving Speaker Diarization using Semantic Information: Joint Pairwise
Constraints Propagation [53.01238689626378]
We propose a novel approach to leverage semantic information in speaker diarization systems.
We introduce spoken language understanding modules to extract speaker-related semantic information.
We present a novel framework to integrate these constraints into the speaker diarization pipeline.
arXiv Detail & Related papers (2023-09-19T09:13:30Z) - Residual Information in Deep Speaker Embedding Architectures [4.619541348328938]
This paper introduces an analysis over six sets of speaker embeddings extracted with some of the most recent and high-performing DNN architectures.
The dataset includes 46 speakers uttering the same set of prompts, recorded in either a professional studio or their home environments.
The results show that the discriminative power of the analyzed embeddings is very high, yet across all the analyzed architectures, residual information is still present in the representations.
arXiv Detail & Related papers (2023-02-06T12:37:57Z) - Joint Speaker Counting, Speech Recognition, and Speaker Identification
for Overlapped Speech of Any Number of Speakers [38.3469744871394]
We propose an end-to-end speaker-attributed automatic speech recognition model.
It unifies speaker counting, speech recognition, and speaker identification on overlapped speech.
arXiv Detail & Related papers (2020-06-19T02:05:18Z) - From Speaker Verification to Multispeaker Speech Synthesis, Deep
Transfer with Feedback Constraint [11.982748481062542]
This paper presents a system involving feedback constraint for multispeaker speech synthesis.
We manage to enhance the knowledge transfer from the speaker verification to the speech synthesis by engaging the speaker verification network.
The model is trained and evaluated on publicly available datasets.
arXiv Detail & Related papers (2020-05-10T06:11:37Z) - Speaker Diarization with Lexical Information [59.983797884955]
This work presents a novel approach for speaker diarization to leverage lexical information provided by automatic speech recognition.
We propose a speaker diarization system that can incorporate word-level speaker turn probabilities with speaker embeddings into a speaker clustering process to improve the overall diarization accuracy.
arXiv Detail & Related papers (2020-04-13T17:16:56Z) - Speech Enhancement using Self-Adaptation and Multi-Head Self-Attention [70.82604384963679]
This paper investigates a self-adaptation method for speech enhancement using auxiliary speaker-aware features.
We extract a speaker representation used for adaptation directly from the test utterance.
arXiv Detail & Related papers (2020-02-14T05:05:36Z) - Improving speaker discrimination of target speech extraction with
time-domain SpeakerBeam [100.95498268200777]
SpeakerBeam exploits an adaptation utterance of the target speaker to extract his/her voice characteristics.
SpeakerBeam sometimes fails when speakers have similar voice characteristics, such as in same-gender mixtures.
We show experimentally that these strategies greatly improve speech extraction performance, especially for same-gender mixtures.
arXiv Detail & Related papers (2020-01-23T05:36:06Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.