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The need for Automatic Speech Command Recognition (ASCR / ASR) on IoT devices is gaining traction because of the increased interest in non-touch-based applications. This article introduces a new lightweight convolutional neural network (CNN) for ASR on microcontrollers.
  • Lijo Jacob
  • May 10, 2021

A new lightweight CNN model for Automatic Speech Command Recognition on Microcontrollers

Abstract

The need for Automatic Speech Command Recognition (ASCR / ASR) on IoT devices is gaining traction because of the increased interest in non-touch-based applications. This article introduces a new lightweight convolutional neural network (CNN) for ASR on microcontrollers. The proposed model is comparable to current state-of-the-art networks with a very low parameter count of less than 63k. The new model gives an accuracy of 96.13% on the Google Speech Commands V2 dataset. A comparative study of results on previous models on the same dataset is also presented.

1. Introduction

Currently, ASR is being done using human-computer interfaces like Google speech, Siri, Alexa which require a client-server mode for its operation as the neural network is very computationally expensive. So, for devices with no internet access, doing speech recognition becomes a non-viable option, as the device itself cannot run very computationally expensive networks. In this paper, we present a very lightweight neural network suitable for low-power devices like microcontrollers.

The network should satisfy the following constraints to run on microcontrollers:

  1. Memory Footprint: Very small memory footprint, ranging in the few 10s of KiloBytes
  2. Low Compute Power: Limited MCPS processor cores in the range of a few 10s of MHz to sub-200MHz.
  3. Offline: All processing should be done locally without cloud connectivity

The remaining contents of this paper are organized in the following sections: 2: Model Architecture, 3: Training Methodology, and 4: Results. 

2. Model Architecture

The model is composed using Keras with a Tensorflow backend. An audio file consists of a single word; hence, the current model can be thought of as a classification model.  Each audio file is a single channel mono wav file, sampled to 16000 Hz and provided as the input to the network. 40 band Mel Frequency Cepstral Coefficients (MFCC) features are extracted from the audio sample and fed into the network.  These MFCC features are fed into a custom convolutional neural network (CNN) for generating the classification results.

       Fig 1.0 High-Level Model Architecture

3. Experimental Results

3.1 Model Integration

For all the experiments, the github repository [3] was referred. To maintain uniformity of all experiments, all aspects of the repository except for our custom model was kept the same.

3.2 Experimental Setup

For the experiment, the dataset used is the Google Speech Commands (GSC) 12 class set with the following keywords: ‘_unknown_’, ‘left’, ‘on’, ‘stop’, ‘right’, ‘off’, ‘down’, ‘up’, ‘no’, ‘go’, ‘yes’, ‘_silence_’ [1]. All keywords are sampled at 16kHz and are of duration 1s.

The GSC V2 comprises 36 folders with the dataset split into train, validation, and test based on predefined percentages. 10% of the total dataset is split as a test and 10% as validation, the remaining 80% is categorized as train data. The keywords not belonging to the above-mentioned keyword list are classified as unknowns. The composition of the train and test set is as shown in the table below.

Class Counts Class Counts
on 3086 on 396
right 3019 right 396
stop 3111 stop 411
up 2948 up 425
down 3134 down 406
no 3130 no 405
go 3106 go 402
left 3037 left 412
yes 3228 yes 419
off 2970 off 402
unknown 6154 unknown 816
silence 3077 silence 408


Table 1.0: a) train data counts per class b) test data counts per class

The background noise class present in the Google Speech Commands dataset is not considered for training as a class, but it is mixed with other speech signals to create augmented data.  Silence class is generated by multiplying a random file with zeros and the count of silence class is calculated as 10% of total files in any random folder.  All metrics and methods are in accordance with standard practices [1,2].  The table below is generated with reference from [1].

        Model      Accuracy       Model Size             (Kbits)
DNN 90.6 3576
CNN+strd 95.6 4232
CNN 96.0 4848
GRU(S) 96.3 4744
CRNN(S) 96.5 3736
SVDF 96.9 2832
DSCNN 96.9 3920
TinySpeech-A 94.3 127
TinySpeech-B 91.3 53
LMU1 96.9 1683
LMU2 95.9 361
LMU3 95.0 105
LMU4 92.7 49
IGN-CNN(our model) 96.13 490

Table 2.0 Accuracy Results for different networks, based on GSC V2 dataset

                 Fig 2.0 Scatterplot comparison of various networks: Accuracy vs Model size

4. Conclusion

A new, lightweight CNN-based model for ASR, optimized for embedded microcontroller devices, was developed. We have benchmarked the model against comparable models using the Google Speech Commands V2 dataset. The accuracy results and total model footprint are comparable to the prevalent state-of-the-art models. This model architecture has been deployed on multiple variants of low-cost microcontrollers from leading semiconductor manufacturers

5. References

  1. Peter Blouw, Gurshaant Malik, Benjamin Morcos, Aaron R. Voelker, and Chris Eliasmith “Hardware Aware Training for Efficient Keyword Spotting on General Purpose and Specialized Hardware”, https://arxiv.org/pdf/2009.04465.pdf
  2. Oleg Rybakov, Natasha Kononenko, Niranjan Subrahmanya, Mirko Visontai, Stella Laurenzo, “Streaming keyword spotting on mobile devices”, https://arxiv.org/pdf/2005.06720.pdf
  3. Reference code, https://github.com/google-research/google-research/tree/master/kws_streaming

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      Sujeet is responsible for driving innovation in Automotive software, identifying Automotive technology trends and advancements, evaluating their potential impact, and development of solutions to meet the needs of our Automotive customers.

      At Ignitarium, he was previously responsible for the growth and P&L of the Embedded Business unit focusing on Multimedia, Automotive, and Platform software.

      Prior to joining Ignitarium in 2016, Sujeet has had a career spanning more than 16 years at Wipro. During this stint, he has played diverse roles from Solution Architect to Presales Lead covering various domains. His technical expertise lies in the areas of Telecom, Embedded Systems, Wireless, Networking, SoC modeling, and Automotive. He has been honored as a Distinguished Member of the Technical Staff at Wipro and has multiple patents granted in the areas of Networking and IoT Security.

      Sujeet holds a degree in Computer Science from Government Engineering College, Thrissur.

       

      RAJIN RAVIMONY Distinguished Engineer

      RAJIN RAVIMONY

      Distinguished Engineer

       

      At Ignitarium, Rajin plays the role of Distinguished Engineer for complex SoCs and systems. He's an expert in ARM-based designs having architected more than a dozen SoCs and played hands-on design roles in several tens more. His core areas of specialization include security and functional safety architecture (IEC61508 and ISO26262) of automotive systems, RTL implementation of math intensive signal processing blocks as well as design of video processing and related multimedia blocks.

       

      Prior to Ignitarium, Rajin worked at Wipro Technologies for 14 years where he held roles of architect and consultant for several VLSI designs in the automotive and consumer domains.

       

      Rajin holds an MS in Micro-electronics from BITS Pilani.

       

      SIBY ABRAHAM Executive Vice President, Strategy

      SIBY ABRAHAM

      Executive Vice President, Strategy

       

      As EVP, of Strategy at Ignitarium, Siby anchors multiple functions spanning investor community relations, business growth, technology initiatives as well and operational excellence.

       

      Siby has over 31 years of experience in the semiconductor industry. In his last role at Wipro Technologies, he headed the Semiconductor Industry Practice Group where he was responsible for business growth and engineering delivery for all of Wipro’s semiconductor customers. Prior to that, he held a vast array of crucial roles at Wipro including Chief Technologist & Vice President, CTO Office, Global Delivery Head for Product Engineering Services, Business Head of Semiconductor & Consumer Electronics, and Head of Unified Competency Framework. He was instrumental in growing Wipro’s semiconductor business to over $100 million within 5 years and turning around its Consumer Electronics business in less than 2 years. In addition, he was the Engineering Manager for Enthink Inc., a semiconductor IP-focused subsidiary of Wipro. Prior to that, Siby was the Technical Lead for several of the most prestigious system engineering projects executed by Wipro R&D.

       

      Siby has held a host of deeply impactful positions, which included representing Wipro in various World Economic Forum working groups on Industrial IOT and as a member of IEEE’s IOT Steering Committee.

       

      He completed his MTech. in Electrical Engineering (Information and Control) from IIT, Kanpur and his BTech. from NIT, Calicut

       

      SUDIP NANDY

      Board Member

       

      An accomplished leader with over 40 years of experience, Sudip has helped build and grow companies in India, the US and the UK.

      He has held the post of Independent Director and Board Member for several organizations like Redington Limited, Excelra, Artison Agrotech, GeBBS Healthcare Solutions, Liquid Hub Inc. and ResultsCX.

      Most recently, Sudip was a Senior Advisor at ChrysCapital, a private equity firm where he has also been the Managing Director and Operating Partner for IT for the past 5 years. During his tenure, he has been Executive Chairman of California-headquartered Infogain Corporation and the non-Exec Chair on the board of a pioneering electric-2-wheeler company Ampere Vehicles, which is now a brand of Greaves Cotton Ltd.

      Earlier on in his career, Sudip has been the CEO and then Chairman India for Aricent. Prior to that, he had spent 25+ years in Wipro where he has been the Head of US business, Engineering R&D Services, and later the Head of EU Operations.

      Sudip is an active investor in several interesting startups in India and overseas, which mostly use technology for the social good, encompassing hyperlocal, healthcare, rural development, farmer support and e2W ecosystem. He also spends time as a coach and mentor for several CEOs in this role.

       

      SUJEETH JOSEPH Chief Product Officer

      SUJEETH JOSEPH

      Chief Technology Officer

       

      As CTO, Sujeeth is responsible for defining the technology roadmap, driving IP & solution development, and transitioning these technology components into practically deployable product engineering use cases.

       

      With a career spanning over 30+ years, Sujeeth Joseph is a semiconductor industry veteran in the SoC, System and Product architecture space. At SanDisk India, he was Director of Architecture for the USD $2B Removable Products Group. Simultaneously, he also headed the SanDisk India Patenting function, the Retail Competitive Analysis Group and drove academic research programs with premier Indian academic Institutes. Prior to SanDisk, he was Chief Architect of the Semiconductor & Systems BU (SnS) of Wipro Technologies. Over a 19-year career at Wipro, he has played hands-on and leadership roles across all phases of the ASIC and System design flow.

       

      He graduated in Electronics Engineering from Bombay University in 1991.

       

      SUJITH MATHEW IYPE Co-founder & CTO

      SUJITH MATHEW IYPE

      Co-founder & COO

       

      As Ignitarium's Co-founder and COO, Sujith is responsible for driving the operational efficiency and streamlining process across the organization. He is also responsible for the growth and P&L of the Semiconductor Business Unit.

       

      Apart from establishing a compelling story in VLSI, Sujith was responsible for Ignitarium's foray into nascent technology areas like AI, ML, Computer Vision, and IoT, nurturing them in our R&D Lab - "The Crucible".

       

      Prior to founding Ignitarium, Sujith played the role of a VLSI architect at Wipro Technologies for 13 years. In true hands-on mode, he has built ASICs and FPGAs for the Multimedia, Telecommunication, and Healthcare domains and has provided technical leadership for many flagship projects executed by Wipro.

       

      Sujith graduated from NIT - Calicut in the year 2000 in Electronics and Communications Engineering and thereafter he has successfully completed a one-year executive program in Business Management from IIM Calcutta.

       

      RAMESH SHANMUGHAM Co-founder & COO

      RAMESH SHANMUGHAM

      Co-founder & CRO

      As Co-founder and Chief Revenue Officer of Ignitarium, Ramesh has been responsible for global business and marketing as well as building trusted customer relationships upholding the company's core values.

      Ramesh has over 25 years of experience in the Semiconductor Industry covering all aspects of IC design. Prior to Ignitarium, Ramesh was a key member of the senior management team of the semiconductor division at Wipro Technologies. Ramesh has played key roles in Semiconductor Delivery and Pre-sales at a global level.

      Ramesh graduated in Electronics Engineering from Model Engineering College, Cochin, and has a Postgraduate degree in Microelectronics from BITS Pilani.