# Deep Speaker Embeddings for Far-Field Speaker Recognition on Short Utterances

arXiv: https://arxiv.org/abs/2002.06033 · HTML: https://mlsanity.com/paper/2002.06033/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Feb 2020.

## Built on by (most cited first, 9 of 9)

- [STC speaker recognition systems for the NIST SRE 2021](https://mlsanity.com/paper/2111.02298/) (2021)
- [The Second DISPLACE Challenge : DIarization of SPeaker and LAnguage in Conversational Environments](https://mlsanity.com/paper/2406.09494/) (2024)
- [Robust Speaker Recognition with Transformers Using wav2vec 2.0](https://mlsanity.com/paper/2203.15095/) (2022)
- [Enrollment-stage Backdoor Attacks on Speaker Recognition Systems via Adversarial Ultrasound](https://mlsanity.com/paper/2306.16022/) (2023)
- [End-to-End Mandarin Tone Classification with Short Term Context Information](https://mlsanity.com/paper/2104.05657/) (2021)
- [Speech enhancement with frequency domain auto-regressive modeling](https://mlsanity.com/paper/2309.13537/) (2023)
- [Universal speaker recognition encoders for different speech segments duration](https://mlsanity.com/paper/2210.16231/) (2022)
- [Investigation of Different Calibration Methods for Deep Speaker Embedding based Verification Systems](https://mlsanity.com/paper/2203.15106/) (2022)
- [Multimodal Speaker Identification in Classroom Environments](https://mlsanity.com/paper/2606.13712/) (2026)

## Abstract (arXiv)

Speaker recognition systems based on deep speaker embeddings have achieved significant performance in controlled conditions according to the results obtained for early NIST SRE (Speaker Recognition Evaluation) datasets. From the practical point of view, taking into account the increased interest in virtual assistants (such as Amazon Alexa, Google Home, AppleSiri, etc.), speaker verification on short utterances in uncontrolled noisy environment conditions is one of the most challenging and highly demanded tasks. This paper presents approaches aimed to achieve two goals: a) improve the quality of far-field speaker verification systems in the presence of environmental noise, reverberation and b) reduce the system qualitydegradation for short utterances. For these purposes, we considered deep neural network architectures based on TDNN (TimeDelay Neural Network) and ResNet (Residual Neural Network) blocks. We experimented with state-of-the-art embedding extractors and their training procedures. Obtained results confirm that ResNet architectures outperform the standard x-vector approach in terms of speaker verification quality for both long-duration and short-duration utterances. We also investigate the impact of speech activity detector, different scoring models, adaptation and score normalization techniques. The experimental results are presented for publicly available data and verification protocols for the VoxCeleb1, VoxCeleb2, and VOiCES datasets.