Comparing Speech Recognition Systems (Microsoft API, Google API And CMU Sphinx)

Veton Këpuska. Int. Journal of Engineering Research and Application www.ijera.com
ISSN : 2248-9622, Vol. 7, Issue 3, ( Part -2) March 2017, pp.20-24
www.ijera.com DOI: 10.9790/9622-0703022024 20|P a g e
Comparing Speech Recognition Systems (Microsoft API, Google
API And CMU Sphinx)
Veton Këpuska1
, Gamal Bohouta2
1,2
(Electrical & Computer Engineering Department, Florida Institute of Technology, Melbourne, FL, USA
ABSTRACT
The idea of this paper is to design a tool that will be used to test and compare commercial speech recognition
systems, such as Microsoft Speech API and Google Speech API, with open-source speech recognition systems
such as Sphinx-4. The best way to compare automatic speech recognition systems in different environments is
by using some audio recordings that were selected from different sources and calculating the word error rate
(WER). Although the WER of the three aforementioned systems were acceptable, it was observed that the
Google API is superior.
Keywords: Speech Recognition, Testing Speech Recognition Systems, Microsoft Speech API, Google Speech
API, CMU Sphinx-4 Speech Recognition.
I. INTRODUCTION
Automatic Speech Recognition (ASR) is
commonly employed in everyday applications. “One
of the goals of speech recognition is to
allow natural communication between humans and
computers via speech, where natural implies
similarity to the ways humans interact with each
other” [8]. ASR has provided many systems that
have been used to increase the interaction experience
between users and computers. According to Dale
Isaacs, “Today automatic speech recognition (ASR)
systems and text-to-speech (TTS) systems are quite
well established. These systems, using the latest
technologies, are operating at accuracies in excess of
90%” [6]. Due to the increasing number of ASR
systems, such as Microsoft, Google, Sphinx, WUW,
HTK and Dragon, it becomes very difficult to know
which of them we need. However, this paper shows
the results of testing Microsoft API, Google API,
and Sphinx4 by using a tool that has been designed
and implemented using Java language with some
audio recordings that were selected from a large
number of sources. Also, in comparing those
systems a number of various components were
utilized and evaluated such as the acoustic model,
the language model, and the dictionary.
There are a number of commercial and
open-source systems such as AT&T Watson,
Microsoft API Speech, Google Speech API,
Amazon Alexa API, Nuance Recognizer, WUW,
HTK and Dragon [2]. Three systems were selected
for our evaluation in different environments:
Microsoft API, Google API, and Sphinx-4 automatic
speech recognition systems. Two of the biggest
companies building voice-powered applications are
Google and Microsoft [4]. The Microsoft API and
Google API are the commercial speech recognition
systems whose code is inaccessible, and
Sphinx-4 is one of the ASR systems whose code is
freely available for download [3].
II. THE CMU SPHINX
The Sphinx system has been developed at
Carnegie Mellon University (CMU). Currently,”
CMU Sphinx has a large vocabulary, speaker
independent speech recognition codebase, and its
code is available for download and use” [13]. The
Sphinx has several versions and packages for
different tasks and applications such as Sphinx-2,
Sphinx-3 and Sphinx-4. Also, there are additional
packages such as Pocketsphinx, Sphinxbase,
Sphinxtrain. In this paper, the Sphinx-4 will be
evaluated. The Sphinx-4 has been written by Java
programming language. Moreover,” its structure has
been designed with a high degree of flexibility and
modularity” [13]. According to Juraj Kačur, “The
latest Sphinx-4 is written in JAVA, and Main
theoretical improvements are: support for finite
grammar called Java Speech API grammar, it
doesn’t impose the restriction using the same
structure for all models” [13] [5]. There are three
main components in the Sphinx-4 structure, which
includes the Frontend, the Decoder and the Linguist.
According to Willie Walker and other who have
worked in Sphinx-4, "we created a number of
differing implementations for each module in the
framework. For example, the Frontend
implementations support MFCC, PLP, and LPC
feature extraction; the Linguist implementations
support a variety of language models, including
CFGs, FSTs, and N-Grams; and the Decoder
supports a variety of Search Manager
implementations" [1]. Therefore, Sphinx-4 has the
most recent version of an HMM-based speech and a
RESEARCH ARTICLE OPEN ACCESS

strong acoustic model by using HHM model with
training large vocabulary [2].
III. THE GOOGLE API
Google has improved its speech recognition
by using a new technology in many applications
with the Google App such as Goog411, Voice
Search on mobile, Voice Actions, Voice Input
(spoken input to keypad), Android Developer APIs,
Voice Search on desktop, YouTube transcription and
Translate, Navigate, TTS.
After Google, has used the new technology
that is the deep learning neural networks, Google
achieved an 8 percent error rate in 2015 that is
reduction of more than 23 percent from year 2013.
According to Pichai, senior vice president of
Android, Chrome, and Apps at Google, “We have
the best investments in machine learning over the
past many years. Indeed, Google has acquired
several deep learning companies over the years,
including DeepMind, DNNresearch, and
Jetpac”[11].
IV. THE MICROSOFT API
Microsoft has developed the Speech API
since 1993, the company hired Xuedong (XD)
Huang, Fil Alleva, and Mei-Yuh Hwang “three of
the four people responsible for the Carnegie Mellon
University Sphinx-II speech recognition system,
which achieved fame in the speech world in 1992
due to its unprecedented accuracy. the first Speech
API is (SAPI) 1.0 team in 1994” [12].
Microsoft has continued to develop the
powerful speech API and has released a series of
increasingly powerful speech platforms. The
Microsoft team has released the Speech API (SAPI)
5.3 with Windows Vista which was very powerful
and useful. On the developer front, "Windows Vista
includes a new WinFX® namespace,
System.Speech. This allows developers to easily
speech-enable Windows Forms applications and
apps based on the Windows Presentation
Framework"[12].
Microsoft has focused on increasing
emphasis on speech recognition systems and
improved the Speech API (SAPI) by using a context-
dependent deep neural network hidden Markov
model (CD-DNN-HMM). According to the
researchers who have worked with Microsoft to
improve the Speech API and the CD-DNN-HMM
models, they determined that the large-vocabulary
speech recognition that achieves substantially better
results than a Context-Dependent Gaussian Mixture
Model Hidden Markov mode12]. Just recently
Microsoft announced “Historic Achievement:
Microsoft researchers reach human parity in
conversational speech recognition” [15].
V. EXPERIMENTS
The best way to test the quality of various
ASR systems is to calculate the word error rate
(WER). According to the WER, we can also test the
different models in the ASR systems, such as the
acoustic model, the language model, and the
dictionary size. However, in this paper we have
developed a tool that we have used to test these
models in Microsoft API, Google API, and Sphinx-
4. Also, we have calculated the WER by using this
tool to recognize a list of sentences, which we
collected in the form of audio files and text
translation. In this paper, we follow these steps to
design the tool and test Microsoft API, Google API,
and Sphinx-4.
VI. TESTING DATA
The audio files were selected from various
sources to evaluate the Microsoft API, Google API,
and Sphinx-4. According to CMUSphin, Sphinx-4's
decoder supports only one of the two specific audio
formats (16000 Hz / 8000 Hz) [13]. Also, Google
does not recognize the WAV format generally used
with Sphinx-4. Part of the process of recognizing
WAV files with Google involves converting the
WAV files to the FLAC format. Microsoft can
recognize any WAV files format. However, we
solved this problem by making our tool recognize all
audio files in the same format (16000 Hz / 8000 Hz).
Some of the audio files have been selected
from the TIMIT corpus.” The TIMIT corpus of read
speech is designed to provide speech data for
acoustic-phonetic studies and for the development
and evaluation of automatic speech recognition
systems. TIMIT contains broadband recordings of
630 speakers of eight major dialects of American
English, each reading ten phonetically rich
sentences” [14]. “The TIMIT corpus includes time-
aligned orthographic, phonetic and word
transcriptions as well as a 16-bit, 16kHz speech
waveform file for each utterance. Corpus design was
a joint effort among the Massachusetts Institute of
Technology (MIT), SRI International (SRI) and
Texas Instruments, Inc. (TI)” [9].
Also, we have selected other audio files
from ITU (International Telecommunication Union)
which is the United Nations Specialized Agency in
the field of telecommunications [10]. Example of
some of the audio files are presented in the table1
below:

Table 1. The Audio Files
VII. SYSTEM DESCRIPTION
This system has been designed by using the
Java language, which is the same language that has
been used in Sphinx-4, as well as the C# that was
used to test the Microsoft API and Google API.
Also, we have used several libraries such as Text to
Speech API, Graph API and Math API for different
tasks. Moreover, this tool was connected with the
classes of Sphinx4, Microsoft API and Google API
to work together to recognize the audio files. Then
we compared the recognition results with the
original recording texts.
Figure 1. The System Interface.
VIII. EXPERIMENTAL RESULTS
The audio recordings with the original
sentences were used to test the Sphinx-4, Microsoft
API, and Google API. By using our tool, we have
tested all files and calculated the word error rate
(WER) and accuracy. We calculated the word error
rate (WER) and accuracy according to these
equations.
WER = (I + D + S) / N
WER = (0 + 0 + 1) / 9 = 0.11
where I words were inserted, D words were deleted,
and S words were substituted.
The original text (Reference):
the small boy PUT the worm on the hook
The recognition text (Hypothesis):
the small boy THAT the worm on the hook
Accuracy = (N - D - S) / N
WA = (9 + 0 + 1) / 9 = 0.88
The original text (Reference):
the coffee STANDARD is too high for the couch
The recognition text (Hypothesis):
the coffee STAND is too high for the couch
Figure 2. The Structure of The System.
Figure 3. The Result of Sphinx-4
By using our tool, we have gathered data and
results are as follows: The Sphinx-4 (37% WER),
Google Speech API (9% WER) and Microsoft
Speech API (18% WER). Where S sentences, N
words, I words were inserted, D words were deleted,
and S words were substituted. CW correct words,
EW error words.

Table 3. The Final Results of Sphinx-4
Table 4. The Final Results of Microsoft API
Table 5. The Final Results of Google API
Table 6. Comparison Between Three Systems
Figure 4. Comparison Between Three Systems
IX. CONCLUSION
In this paper, it can be concluded that the
tool that we have built to test the Sphinx-4,
Microsoft API, and Google API by using some
audio recordings that were selected from many
places with the original sentences showed that
Sphinx-4 achieved 37% WER, Microsoft API
achieved 18% WER and Google API achieved 9%
WER. Therefore, it can be stated that the acoustic
modeling and language model of Google is superior.
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