Improving speaker turn embedding by crossmodal transfer learning from face embedding

Learning speaker turn embeddings has shown considerable improvement in situations where conventional speaker modeling approaches fail. However, this improvement is relatively limited when compared to the gain observed in face embedding learning, which has been proven very successful for face verification and clustering tasks. Assuming that face and voices from the same identities share some latent properties (like age, gender, ethnicity), we propose three transfer learning approaches to leverage the knowledge from the face domain (learned from thousands of images and identities) for tasks in the speaker domain. These approaches, namely target embedding transfer, relative distance transfer, and clustering structure transfer, utilize the structure of the source face embedding space at different granularities to regularize the target speaker turn embedding space as optimizing terms. Our methods are evaluated on two public broadcast corpora and yield promising advances over competitive baselines in verification and audio clustering tasks, especially when dealing with short speaker utterances. The analysis of the results also gives insight into characteristics of the embedding spaces and shows their potential applications

Processing and Linking Audio Events in Large Multimedia Archives: The EU inEvent Project

In the inEvent EU project [1], we aim at structuring, retrieving, and sharing large archives of networked, and dynamically changing, multimedia recordings, mainly consisting of meetings, videoconferences, and lectures. More specifically, we are developing an integrated system that performs audiovisual processing of multimedia recordings, and labels them in terms of interconnected “hyper-events ” (a notion inspired from hyper-texts). Each hyper-event is composed of simpler facets, including audio-video recordings and metadata, which are then easier to search, retrieve and share. In the present paper, we mainly cover the audio processing aspects of the system, including speech recognition, speaker diarization and linking (across recordings), the use of these features for hyper-event indexing and recommendation, and the search portal. We present initial results for feature extraction from lecture recordings using the TED talks. Index Terms: Networked multimedia events; audio processing: speech recognition; speaker diarization and linking; multimedia indexing and searching; hyper-events. 1

Jitter and Shimmer measurements for speaker diarization

Jitter and shimmer voice quality features have been successfully used to characterize speaker voice traits and detect voice pathologies. Jitter and shimmer measure variations in the fundamental frequency and amplitude of speaker's voice, respectively. Due to their nature, they can be used to assess differences between speakers. In this paper, we investigate the usefulness of these voice quality features in the task of speaker diarization. The combination of voice quality features with the conventional spectral features, Mel-Frequency Cepstral Coefficients (MFCC), is addressed in the framework of Augmented Multiparty Interaction (AMI) corpus, a multi-party and spontaneous speech set of recordings. Both sets of features are independently modeled using mixture of Gaussians and fused together at the score likelihood level. The experiments carried out on the AMI corpus show that incorporating jitter and shimmer measurements to the baseline spectral features decreases the diarization error rate in most of the recordings.Peer ReviewedPostprint (published version

The Blame Game: Performance Analysis of Speaker Diarization System Components

In this paper we discuss the performance analysis of a speaker diarization system similar to the system that was submitted by ICSI at the NIST RT06s evaluation benchmark. The analysis that is based on a series of oracle experiments, provides a good understanding of the performance of each system component on a test set of twelve conference meetings used in previous NIST benchmarks. Our analysis shows that the speech activity detection component contributes most to the total diarization error rate (23%). The lack of ability to model verlapping speech is also a large source of errors (22%) followed by the component that creates the initial system models (15%)