Machine learning has been evolving rapidly around the world. More and more corporations are making their work available to the public. Developers can use machine learning to innovate in creating smart assistants for voice analysis.
“Voice is the future. The world’s technology giants are clamoring for vital market share, with both Google and Amazon placing voice-enabled devices at the core of their strategy.”
— Clark Boyd, a Content Marketing Specialist in NYC
Machine learning has led to major advances in voice recognition. Google has combined the latest technology with cloud computing power to share data and improve the accuracy of machine learning algorithms.
You don't even need to be a programmer to create a simple voice assistant. All you need to do is define what features you want your assistant to have and what tasks it will have to do for you. Then you can use Python libraries to leverage other developers’ models, simplifying the process of writing your bot. Several corporations build and use these assistants to streamline initial communications with their customers.
Python Audio Analysis
The need to process audio content continues to grow with the emergence of the latest game-changing products, such as Google Home and Alexa. As such, working with audio data has become a new direction and research area for developers around the world.
"You don't have to dial into a conference call anymore," Amazon CTO Werner Vogels said. “Just say, 'Alexa, start the meeting.’"
Speech recognition is the process of converting spoken words into text. Python supports many speech recognition engines and APIs, including Google Speech Engine and Google Cloud Speech API.
Possible applications extend to voice recognition, music classification, tagging, and generation and pave the way to Python SciPy for audio use scenarios that will be the new era of deep learning.
Audio content plays a significant role in the digital world. Hence, we need modules that can analyze the quality of such content. Voice assistants are one way of interacting with voice content. With their help, you can perform a variety of actions without resorting to complicated searches. All you have to do is talk to the assistant, and it reacts in a matter of seconds.
Speech recognition requires audio input. SpeechRecognition makes it easy to get that input understood by machines. Instead of creating scripts to access microphones and process audio files from scratch, SpeechRecognition lets you get started in just a few minutes. However, Keras signal processing, an open-source software library that provides a Spectrogram Python interface for artificial neural networks, can also help in the speech recognition process. Just have a look at Keras tutorials.
With what primary functions can you empower your Python-based voice assistant?
- Recognize and analyze human speech.
- Report the current weather forecast anywhere in the world.
- Search on Google or YouTube.
- Translate phrases from the target language into your native language and vice versa.
- Say hello and goodbye to turn on and off accordingly.
- Change language recognition and speech synthesis settings.
“These days, speech recognition is incredibly important. It is an additional opportunity to erase barriers and inconveniences between people, as well as to solve many problems in speech analysis and synthesis processes.”
— Vlad Medvedovsky at Proxet, custom software development solutions company
Python Libraries for Work
Python already has many useful sound processing libraries and several built-in modules for basic sound functions. For example, let's take a look at the Python Librosa, pocketsphinx, and pyAudioAnalysis libraries.
Librosa is a Python library for analyzing audio signals, with a specific focus on music and voice recognition. Librosa includes the nuts and bolts for building a music information retrieval (MIR) system. Many manuals, documentation files, and tutorials cover this library, so it shouldn't be too hard to figure out
Pocketsphinx can recognize speech from the microphone and from a file. It can also search for hot phrases. What makes pocketsphinx different from cloud-based solutions is that it works offline and can function on a limited vocabulary, resulting in increased accuracy. If you're interested, there are some examples on the library page. Note the "Default config" item.
pyAudioAnalysis is an open-source Python library. This module provides the ability to perform many operations to analyze audio signals, including:
- feature extraction
- classification of received audio signals
- supervised and unsupervised segmentation and audio content analysis
pyAudioAnalysis has a long and successful history of use in several research applications for audio analysis, such as:
- smart home functions through sound event detection,
- emotion recognition in speech,
- classification of depression based on audio-visual features,
- music segmentation.
pyAudioAnalysis assumes that audio files are organized into folders, and each folder represents a separate audio class.
Deep Learning Audio
Audio deep learning analysis is the understanding of audio signals captured by digital devices using apps.
Applications include customer satisfaction analysis on help desk calls, media content analysis and retrieval, medical diagnostic tools and patient monitoring, assistive technology for the hearing impaired, and sound analysis for public safety.
Real-Life Examples from Business
Python-based tools for speech recognition have long been under development and are already successfully used worldwide. Speech synthesis and machine recognition have been a fascinating topic for scientists and engineers for many years. Inspired by talking and hearing machines in science fiction, we have experienced rapid and sustained technological development in recent years. Custom software development solutions can be a useful tool for implementing voice recognition in your business.
“Voice search has long been the aim of brands, and research now shows that it is coming to fruition. I admit I was skeptical about the impact of voice. Still, the stories of my children and those of my colleagues bring home one of the most misunderstood parts of the mobile revolution."
— Alex Robbio, President and co-founder of Belatrix Software
Each case of the voice assistant use is unique. To some, it helps to communicate with gadgets. According to the PwC study, more than half of smartphone users give voice commands to devices. Among adults (25-49 years), the proportion of those who regularly use voice interfaces is even higher than among young people (18-25): 59% vs. 65%, respectively.
In 1996, IBM MedSpeak was released. Since then, voice recognition has been used for medical history recording and making notes while examining scans. Taking notes using voice recognition, a medic can work without interruptions to write on a computer or a paper chart.
For example, Toshiba takes major steps towards inclusion and accessibility, with features for employees with hearing impairments. There is a corporate program called the Universal Design Advisor System, in which people with different types of disabilities participate in the development of Toshiba products.
In addition, we can look at examples from the banking industry. Voice banking can significantly reduce the need for personnel costs and human customer service. A personalized banking assistant can also considerably increase customer satisfaction and loyalty.
Voice recognition has also helped marketers for years. The main impact of voice assistants in marketing is particularly noticeable in categories such as:
- Big data analysis. Thanks to voice recognition with SciPy audio processing, marketers can access a new type of data for analysis. The accents of users from different countries, speech patterns, and people's vocabulary can help to interpret customers' locations. Additionally, big data analysis makes it possible to recognize the age and features of demographic characteristics.
- User behavior. Conversational speech allows for performing longer searches, which leads to a change in the size of goofy search queries from users. Marketers should now focus on longer search queries to thoroughly analyze a product's market behavior or service's target audience.
And perhaps the most common example of human speech transformation is the use of speech synthesis tools to eliminate language barriers between people. Reducing misunderstandings between business representatives opens broader horizons for cooperation, helps erase cultural boundaries, and greatly facilitates the negotiation process.
Proxet is already able to provide software for voice recognition. The company's experienced specialists can create a special voice assistant for your project to solve important tasks.
Learn a step-by-step framework for constructing an optimal modern data stack — hear Proxet's CTO cover crucial elements like build vs buy choices, open source tools, typical mistakes, and how we can assist.
Build a modern data stack by following best practices from data engineering experts. Learn about data maturity, data stack components, and how to build.