Blog
The latest from Google Research
Joint Speech Recognition and Speaker Diarization via Sequence Transduction
Friday, August 16, 2019
Posted by Laurent El Shafey, Software Engineer and Izhak Shafran, Research Scientist, Google Health
Being able to recognize “who said what,” or
speaker diarization
, is a critical step in understanding audio of human dialog through automated means. For instance, in a medical conversation between doctors and patients, “
Yes
” uttered by a patient in response to “
Have you been taking your heart medications regularly?
” has a substantially different implication than a rhetorical “
Yes?
” from a physician.
Conventional speaker diarization (SD) systems use two stages, the first of which detects changes in the acoustic spectrum to determine when the speakers in a conversation change, and the second of which identifies individual speakers across the conversation.
This basic multi-stage approach
is almost two decades old, and during that time only the speaker change detection component has improved.
With the recent development of a novel neural network model—the
recurrent neural network transducer
(RNN-T)—we now have a suitable architecture to improve the performance of speaker diarization addressing some of the limitations of the previous diarization system we
presented recently
. As reported in our recent paper, “
Joint Speech Recognition and Speaker Diarization via Sequence Transduction
,” to be presented at
Interspeech 2019
, we have developed an RNN-T based speaker diarization system and have demonstrated a breakthrough in performance from about 20% to 2% in word diarization error rate—a factor of 10 improvement.
Conventional Speaker Diarization Systems
Conventional speaker diarization systems rely on differences in how people sound acoustically to distinguish the speakers in the conversations. While male and female speakers can be identified relatively easily from their pitch using simple acoustic models (e.g.,
Gaussian mixture models
) in a single stage, speaker diarization systems use a multi-stage approach to distinguish between speakers having potentially similar pitch. First, a change detection algorithm breaks up the conversation into homogeneous segments, hopefully containing only a single speaker, based upon detected vocal characteristics. Then, deep learning models are employed to map segments from each speaker to an embedding vector. Finally, in a clustering stage, these embeddings are grouped together to keep track of the same speaker across the conversation.
In practice, the speaker diarization system runs in parallel to the automatic speech recognition (ASR) system and the outputs of the two systems are combined to attribute speaker labels to the recognized words.
Conventional speaker diarization system infers speaker labels in the acoustic domain and then overlays the speaker labels on the words generated by a separate ASR system.
There are several limitations with this approach that have hindered progress in this field. First, the conversation needs to be broken up into segments that only contain speech from one speaker. Otherwise, the embedding will not accurately represent the speaker. In practice, however, the change detection algorithm is imperfect, resulting in segments that may contain multiple speakers. Second, the clustering stage requires that the number of speakers be known and is particularly sensitive to the accuracy of this input. Third, the system needs to make a very difficult trade-off between the segment size over which the voice signatures are estimated and the desired model accuracy. The longer the segment, the better the quality of the voice signature, since the model has more information about the speaker. This comes at the risk of attributing short interjections to the wrong speaker, which could have very high consequences, for example, in the context of processing a clinical or financial conversation where affirmation or negation needs to be tracked accurately. Finally, conventional speaker diarization systems do not have an easy mechanism to take advantage of linguistic cues that are particularly prominent in many natural conversations. An utterance, such as “
How often have you been taking the medication?
” in a clinical conversation is most likely uttered by a medical provider, not a patient. Likewise, the utterance, “
When should we turn in the homework?
” is most likely uttered by a student, not a teacher. Linguistic cues also signal high probability of changes in speaker turns, for example, after a question.
There are a few exceptions to the conventional speaker diarization system, and one such exception was reported in our
recent blog post
. In that work, the hidden states of the recurrent neural network (RNN) tracked the speakers, circumventing the weakness of the clustering stage. The work reported in this post takes a different approach and incorporates linguistic cues, as well.
An Integrated Speech Recognition and Speaker Diarization System
We developed a novel and simple model that not only combines acoustic and linguistic cues seamlessly, but also combines speaker diarization and speech recognition into one system. The integrated model does not degrade the speech recognition performance significantly compared to an equivalent recognition only system.
The key insight in our work was to recognize that the RNN-T architecture is well-suited to integrate acoustic and linguistic cues. The RNN-T model consists of three different networks: (1) a transcription network (or
encoder
) that maps the acoustic frames to a latent representation, (2) a prediction network that predicts the next target label given the previous target labels, and (3) a joint network that combines the output of the previous two networks and generates a probability distribution over the set of output labels at that time step. Note, there is a feedback loop in the architecture (diagram below) where previously recognized words are fed back as input, and this allows the RNN-T model to incorporate linguistic cues, such as the end of a question.
An integrated speech recognition and speaker diarization system where the system jointly infers who spoke when and what.
Training the RNN-T model on accelerators like graphical processing units (GPU) or tensor processing units (TPU) is non-trivial as computation of the
loss function
requires running the
forward-backward algorithm
, which includes all possible alignments of the input and the output sequences. This issue was addressed recently in a
TPU friendly implementation
of the forward-backward algorithm, which recasts the problem as a sequence of matrix multiplications. We also took advantage of an
efficient implementation of the RNN-T loss
in TensorFlow that allowed quick iterations of model development and trained a very deep network.
The integrated model can be trained just like a speech recognition system. The reference transcripts for training contain words spoken by a speaker followed by a tag that defines the role of the speaker. For example, “
When is the homework due?
” ≺student≻, “
I expect you to turn them in tomorrow before class
,” ≺teacher≻. Once the model is trained with examples of audio and corresponding reference transcripts, a user can feed in the recording of the conversation and expect to see an output in a similar form. Our analyses show that improvements from the RNN-T system impact all categories of errors, including short speaker turns, splitting at the word boundaries, incorrect speaker assignment in the presence of overlapping speech, and poor audio quality. Moreover, the RNN-T system exhibited consistent performance across conversation with substantially lower variance in average error rate per conversation compared to the conventional system.
A comparison of errors committed by the conventional system vs. the RNN-T system, as categorized by human annotators.
Furthermore, this integrated model can predict other labels necessary for generating more reader-friendly ASR transcripts. For example, we have been able to successfully improve our transcripts with punctuation and capitalization symbols using the appropriately matched training data. Our outputs have lower punctuation and capitalization errors than our previous models that were separately trained and added as a post-processing step after ASR.
This model has now become a standard component in our project on
understanding medical conversations
and is also being adopted more widely in our non-medical speech services.
Acknowledgements
We would like to thank Hagen Soltau without whose contributions this work would not have been possible. This work was performed in collaboration with Google Brain and Speech teams.
Labels
accessibility
ACL
ACM
Acoustic Modeling
Adaptive Data Analysis
ads
adsense
adwords
Africa
AI
AI for Social Good
Algorithms
Android
Android Wear
API
App Engine
App Inventor
April Fools
Art
Audio
Augmented Reality
Australia
Automatic Speech Recognition
AutoML
Awards
BigQuery
Cantonese
Chemistry
China
Chrome
Cloud Computing
Collaboration
Compression
Computational Imaging
Computational Photography
Computer Science
Computer Vision
conference
conferences
Conservation
correlate
Course Builder
crowd-sourcing
CVPR
Data Center
Data Discovery
data science
datasets
Deep Learning
DeepDream
DeepMind
distributed systems
Diversity
Earth Engine
economics
Education
Electronic Commerce and Algorithms
electronics
EMEA
EMNLP
Encryption
entities
Entity Salience
Environment
Europe
Exacycle
Expander
Faculty Institute
Faculty Summit
Flu Trends
Fusion Tables
gamification
Gboard
Gmail
Google Accelerated Science
Google Books
Google Brain
Google Cloud Platform
Google Docs
Google Drive
Google Genomics
Google Maps
Google Photos
Google Play Apps
Google Science Fair
Google Sheets
Google Translate
Google Trips
Google Voice Search
Google+
Government
grants
Graph
Graph Mining
Hardware
HCI
Health
High Dynamic Range Imaging
ICCV
ICLR
ICML
ICSE
Image Annotation
Image Classification
Image Processing
Inbox
India
Information Retrieval
internationalization
Internet of Things
Interspeech
IPython
Journalism
jsm
jsm2011
K-12
Kaggle
KDD
Keyboard Input
Klingon
Korean
Labs
Linear Optimization
localization
Low-Light Photography
Machine Hearing
Machine Intelligence
Machine Learning
Machine Perception
Machine Translation
Magenta
MapReduce
market algorithms
Market Research
materials science
Mixed Reality
ML
ML Fairness
MOOC
Moore's Law
Multimodal Learning
NAACL
Natural Language Processing
Natural Language Understanding
Network Management
Networks
Neural Networks
NeurIPS
Nexus
Ngram
NIPS
NLP
On-device Learning
open source
operating systems
Optical Character Recognition
optimization
osdi
osdi10
patents
Peer Review
ph.d. fellowship
PhD Fellowship
PhotoScan
Physics
PiLab
Pixel
Policy
Professional Development
Proposals
Public Data Explorer
publication
Publications
Quantum AI
Quantum Computing
Recommender Systems
Reinforcement Learning
renewable energy
Research
Research Awards
resource optimization
Responsible AI
Robotics
schema.org
Search
search ads
Security and Privacy
Self-Supervised Learning
Semantic Models
Semi-supervised Learning
SIGCOMM
SIGMOD
Site Reliability Engineering
Social Networks
Software
Sound Search
Speech
Speech Recognition
statistics
Structured Data
Style Transfer
Supervised Learning
Systems
TensorBoard
TensorFlow
TPU
Translate
trends
TTS
TV
UI
University Relations
UNIX
Unsupervised Learning
User Experience
video
Video Analysis
Virtual Reality
Vision Research
Visiting Faculty
Visualization
VLDB
Voice Search
Wiki
wikipedia
WWW
Year in Review
YouTube
Archive
2022
May
Apr
Mar
Feb
Jan
2021
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2020
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2019
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2018
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2017
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2016
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2015
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2014
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2013
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2012
Dec
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2011
Dec
Nov
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2010
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2009
Dec
Nov
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2008
Dec
Nov
Oct
Sep
Jul
May
Apr
Mar
Feb
2007
Oct
Sep
Aug
Jul
Jun
Feb
2006
Dec
Nov
Sep
Aug
Jul
Jun
Apr
Mar
Feb
Feed
Follow @googleai
Give us feedback in our
Product Forums
.
