Text-independent speaker recognition using short utterances is a highly
challenging task due to the large variation and content mismatch between short
utterances. I-vector based systems have become the standard in speaker
verification applications, but they are less effective with short utterances.
In this paper, we first compare two state-of-the-art universal background model
training methods for i-vector modeling using full-length and short utterance
evaluation tasks. The two methods are Gaussian mixture model (GMM) based and
deep neural network (DNN) based methods. The results indicate that the
I-vector_DNN system outperforms the I-vector_GMM system under various
durations. However, the performances of both systems degrade significantly as
the duration of the utterances decreases. To address this issue, we propose two
novel nonlinear mapping methods which train DNN models to map the i-vectors
extracted from short utterances to their corresponding long-utterance
i-vectors. The mapped i-vector can restore missing information and reduce the
variance of the original short-utterance i-vectors. The proposed methods both
model the joint representation of short and long utterance i-vectors by using
autoencoder. Experimental results using the NIST SRE 2010 dataset show that
both methods provide significant improvement and result in a max of 28.43%
relative improvement in Equal Error Rates from a baseline system, when using
deep encoder with residual blocks and adding an additional phoneme vector. When
further testing the best-validated models of SRE10 on the Speaker In The Wild
dataset, the methods result in a 23.12% improvement on arbitrary-duration (1-5
s) short-utterance conditions.

本文提出了两种新的非线性映射方法，使用 DNN 模型将从短语音中提取的 i-vector 映射到相应的长语音 i-vector，实现了从短语音中还原丢失信息和减少 i-vector 的方差，可在说话人识别中实现 28.43％的最大相对改进。

基于深度神经网络的短语音验证 i-vector 映射

Deep neural network based i-vector mapping for speaker verification  using short utterances

The Multitarget Challenge aims to assess how well current speech technology
is able to determine whether or not a recorded utterance was spoken by one of a
large number of 'blacklisted' speakers. It is a form of multi-target speaker
detection based on real-world telephone conversations. Data recordings are
generated from call center customer-agent conversations. Each conversation is
represented by a single i-vector. Given a pool of training and development data
from non-Blacklist and Blacklist speakers, the task is to measure how
accurately one can detect 1) whether a test recording is spoken by a Blacklist
speaker, and 2) which specific Blacklist speaker was talking.

该研究旨在评估当前语音技术在确定一条录制的话语是否由许多 “黑名单” 发言者之一说出方面的能力，其数据来源于客服中心客户 - 代理商对话，并且任务是根据来自非黑名单和黑名单发言者的训练和开发数据池，测量出 1）一个测试记录是否由黑名单发言者说出，以及 2）究竟是哪位特定的黑名单发言者在说话。

MCE 2018: 第一届多目标说话人检测和识别挑战评估计划、数据集和基线系统

MCE 2018: The 1st Multi-target Speaker Detection and Identification  Challenge Evaluation (MCE) Plan, Dataset and Baseline System

Many existing speaker verification systems are reported to be vulnerable
against different spoofing attacks, for example speaker-adapted speech
synthesis, voice conversion, play back, etc. In order to detect these spoofed
speech signals as a countermeasure, we propose a score level fusion approach
with several different i-vector subsystems. We show that the acoustic level
Mel-frequency cepstral coefficients (MFCC) features, the phase level modified
group delay cepstral coefficients (MGDCC) and the phonetic level phoneme
posterior probability (PPP) tandem features are effective for the
countermeasure. Furthermore, feature level fusion of these features before
i-vector modeling also enhance the performance. A polynomial kernel support
vector machine is adopted as the supervised classifier. In order to enhance the
generalizability of the countermeasure, we also adopted the cosine similarity
and PLDA scoring as one-class classifications methods. By combining the
proposed i-vector subsystems with the OpenSMILE baseline which covers the
acoustic and prosodic information further improves the final performance. The
proposed fusion system achieves 0.29% and 3.26% EER on the development and test
set of the database provided by the INTERSPEECH 2015 automatic speaker
verification spoofing and countermeasures challenge.

提出了一种基于多种 i-vector 子系统的得分融合方法，通过采用声学水平的 Mel 频率倒谱系数（MFCC）特征、相位水平的修改群延迟倒谱系数（MGDCC）和语音学水平的音素后验概率（PPP）串联特征来检测并对抗欺骗性语音信号。