Trending Now: Far-Field Speech and Voice Recognition Market Analysis | By Company Profiles | Size | Share | Growth | Trends and Forecast To 2024

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The Far-Field Speech and Voice Recognition Market is expected to grow from USD 969 million in 2018 to USD 3,505 million by 2024, at a CAGR of 23.89%. This report spread across 140 Pages, Profiling 10 Companies and Supported with 63 Tables and 42 figures are now available in this research.

Top Key Players Profiled in the Far-Field Speech and Voice Recognition Market:

  • Synaptics (US)
  • Texas Instruments (US)
  • Qualcomm (US)
  • Sensory (US)
  • STMicroelectronics (Switzerland)
  • Harman (US)
  • Andrea Electronics (US)
  • Cirrus Logic (US)
  • Microsemi (US)
  • DSP Group (US)


Software or deep learning plays a major role in voice pickup. It offers beam forming, spectral noise reduction, acoustic echo cancellation, and de-reverberation technologies to enhance speech recognition performance in far-field use cases and hence improve the quality of the input speech signal.


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Far-field speech and voice recognition systems use multiple microphones in form of a linear or circular array to reduce the impact of noise and reverberation from the surrounding environment.  In recent years the development of deep-learning-based trainable algorithm for far-field microphones has greatly reduced the limitation of signal distortion and loss of information. Microphones with machine learning, neural networks, and beam forming have improved front-end voice processing.


Table of Contents:

1 Introduction

1.1 Objectives of the Study

1.2 Definition

1.3 Scope of the Study

1.3.1 Markets Covered

1.3.2 Geographic Scope

1.3.3 Years Considered for the Study

1.4 Currency

1.5 Limitations

1.6 Stakeholders


2 Research Methodology

2.1 Research Data

2.1.1 Secondary Data Secondary Sources

2.1.2 Primary Data Key Data From Primary Sources Key Industry Insights Breakdown of Primaries

2.2 Market Size Estimation

2.2.1 Bottom-Up Approach

2.2.2 Top-Down Approach

2.3 Market Breakdown and Data Triangulation

2.4 Research Assumptions


3 Executive Summary


4 Premium Insights


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