• Skip to primary navigation
  • Skip to main content
SRI logo
  • About
    • Press room
    • Our history
  • Expertise
    • Advanced imaging systems
    • Artificial intelligence
    • Biomedical R&D services
    • Biomedical sciences
    • Computer vision
    • Cyber & formal methods
    • Education and learning
    • Innovation strategy and policy
    • National security
    • Ocean & space
    • Quantum
    • Robotics, sensors & devices
    • Speech & natural language
    • Video test & measurement
  • Ventures
  • NSIC
  • Careers
  • Contact
  • 日本支社
Search
Close
Speech & natural language publications October 1, 2015

Study of senone-based deep neural network approaches for spoken language recognition

Mitchell McLaren

Citation

Copy to clipboard


L. Ferrer, Y. Lei, M. McLaren and N. Scheffer, “Study of senone-based deep neural network approaches for spoken language recognition,” in IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. PP, Issue 99, pp. 1-1.

Abstract

This paper compares different approaches for using deep neural networks (DNNs) trained to predict senone posteriors for the task of spoken language recognition (SLR).  These approaches have recently been found to outperform various baseline systems on different datasets, but they have not yet been compared to each other or to a common baseline.  Two of these approaches use the DNNs to generate feature vectors which are then processed in different ways to predict the score of each language given a test sample.  The features are extracted either from a bottleneck layer in the DNN or from the output layer.  In the third approach, the standard i-vector extraction procedure is modified to use the senones as classes and the DNN to predict the zero-th order statistics. We compare these three approaches and conclude that the approach based on bottleneck features followed by i-vector modeling outperform the other two approaches.  We also show that score-level fusion of some of these approaches leads to gains over using a single approach for short-duration test samples.  Finally, we demonstrate that fusing systems that use DNNs trained with several languages leads to improvements in performance over the best single system, and we propose an adaptation procedure for DNNs trained with languages with less available data.  Overall, we show improvements between 40% and 70% relative to a state-of-the-art Gaussian mixture model (GMM) i-vector system on test durations from 3 seconds to 120 seconds on two significantly different tasks:  the NIST 2009 language recognition evaluation task and the DARPA RATS language identification task.

Index Terms—Spoken Language Recognition, Deep Neural Networks, Senones

↓ Download

Share this
Career call to action image

Work with us

Search jobs

How can we help?

Once you hit send…

We’ll match your inquiry to the person who can best help you.

Expect a response within 48 hours.

Our work

Case studies

Publications

Timeline of innovation

Areas of expertise

Institute

Leadership

Press room

Media inquiries

Compliance

Careers

Job listings

Contact

SRI Ventures

Our locations

Headquarters

333 Ravenswood Ave
Menlo Park, CA 94025 USA

+1 (650) 859-2000

Subscribe to our newsletter


日本支社
SRI International
  • Contact us
  • Privacy Policy
  • Cookies
  • DMCA
  • Copyright © 2023 SRI International
Manage Cookie Consent
To provide the best experiences, we use technologies like cookies to store and/or access device information. Consenting to these technologies will allow us to process data such as browsing behavior or unique IDs on this site. Not consenting or withdrawing consent, may adversely affect certain features and functions.
Functional Always active
The technical storage or access is strictly necessary for the legitimate purpose of enabling the use of a specific service explicitly requested by the subscriber or user, or for the sole purpose of carrying out the transmission of a communication over an electronic communications network.
Preferences
The technical storage or access is necessary for the legitimate purpose of storing preferences that are not requested by the subscriber or user.
Statistics
The technical storage or access that is used exclusively for statistical purposes. The technical storage or access that is used exclusively for anonymous statistical purposes. Without a subpoena, voluntary compliance on the part of your Internet Service Provider, or additional records from a third party, information stored or retrieved for this purpose alone cannot usually be used to identify you.
Marketing
The technical storage or access is required to create user profiles to send advertising, or to track the user on a website or across several websites for similar marketing purposes.
Manage options Manage services Manage {vendor_count} vendors Read more about these purposes
View preferences
{title} {title} {title}