Speech Processing: What Is It?
Speech is one of the oldest forms of communication, but for machines to learn how to understand and replicate it, there are a few technologies that are making this possible.
Speech processing powers many of the tools we rely on today, from virtual assistants to transcription platforms. Without speech processing technologies, the systems we know and love wouldn’t be half as effective.
In this post, we’ll explore speech processing in more depth and look at the main techniques it uses as well as the different ways it powers various applications.
What is Speech Processing?
Speech processing is a broad term that’s meant to encompass different studies and applications of digital speech signals. This includes the acquisition, analysis, transfer, manipulation, and storage of speech signals. The field of speech processing involves different methodologies from linguistics, electrical engineering, and computer science.
Speech processing is a distinct field within natural language processing (NLP) that has different use cases and is studied to improve speech processing software related to text-to-speech applications, automatic speech recognition, and speech coding.
4 Key Speech Processing Techniques
Speech processing relies on various techniques to teach machines how to make sense of words and language as a whole. Each technique tackles a specific challenge with understanding and analyzing digital speech samples, whether it’s matching patterns, cleaning up sounds, or connecting patterns to recognize speech in context. Below, we’ll look at four key speech and language processing techniques
Dynamic Time Warping (DTW)
Speech varies in speed, which is one of the factors that makes our speech sound different depending on who is speaking, if when two people are saying the same time. DTW is used to compare two speech signals and look for alignment between them. With the DTW technique, algorithms are trained to look for similarities between two given sequences, even if the speech samples are at different speeds, in order to make matches and comparisons. This technique is especially helpful for speaker verification and to recognize isolated words.
Hidden Markov Models (HMMs)
Hidden Markov Models act as the ‘storyteller’ of speech processing. This technique models speech in a sequence of states, each one representing a specific sound of phoneme. Through advanced algorithms, each state is assigned a probability for transitions and outputs, which is what allows HMMs to guess which sound or word will come next based on patterns it learns. These types of models were essential to the development of speech recognition systems are are still used in some hybrid systems with neural networks.
Artificial Neural Networks
ANNs are machine learning models that have been built based on how the human brain functions. Connected units or nodes, referred to as artificial neurons, mimic the neurons in a real brain. Each connection transmits a signal to the other, setting off large chain reactions that allow ANNs to learn and identify patterns. ANNs can recognize complex patterns in large sets of data, specifically speech data, and are often used in modern speech recognition systems to identify subtle audio data features, transcribe speech, detect sentiment and emotions, and even determine accents.
Phase-Aware Processing
Often, speech processing focuses on the amplitude of a sound wave, but with phase-aware processing, the phase information is also taken into consideration. This is due to the fact that the phase information can help in tasks like improving speech quality and separating a voice from background noise. In speech technologies, phase-aware processing can be helpful for noise reduction and speech isolation to make outputs more accurate.
What Is Speech Processing Used For?
Speech processing bridges the gap between human speech and machine understanding, paving the way for different applications and systems that make our lives easier. From enhancing communication to improving accessibility, below are some applications of speech processing worth noting.
Keyword spotting
Keyword spotting can be used for a few different things. It can help speech technology that needs to identify specific words to trigger certain actions or responses, and it can also be used to activate a device, such as saying “Hey, Siri” before initiating a command to a voice assistant.
Voice and speaker recognition
Speech processing technologies can help identify distinct voices, connecting them with individual speakers in a conversation, also referred to as speaker diarization. By focusing on speech patterns and factors like tone and cadence, technologies can pick up on different speakers, making it easier for technologies like transcription platforms to accurately transcribe conversations with multiple speakers.
Sentiment detection
Another way speech processing is used is to detect emotion and sentiment in text or speech. By analyzing certain words and acoustic features of language, algorithms can detect sentiment in speech, which is helpful for companies looking to better understand customer behavior and feelings towards their brand, products, or services.
Speech synthesis
Machines require speech processing technologies to turn written text into artificial speech. This technology powers text-to-speech systems, which reproduce spoken language that mimics human speech. A lot of applications that focus on accessibility for differently abled individuals rely on these types of systems.
Speech-to-text
Speech-to-text technologies can detect phonemes in audio inputs and match them with patterns, words, and phrases to arrive at a textual output. This type of technology is essential to power dictation platforms and other systems.
Assistive technology
For people with impairments, speech processing is instrumental in powering technologies that make it easier for them to interact with machinery. From speech-controlled devices to voice-guided systems for the visually impaired, assistive platforms make it possible for everyone to benefit from modern technology.
What’s the Difference Between Speech Processing and Speech Recognition?
The difference between speech processing and speech recognition lies in their scope and focus. Speech processing is a broad field that involves analyzing, transforming, and rendering speech signals. It has a range of applications, such as speech synthesis (text-to-speech), speech enhancement (removing noise), speaker identification, and others that we saw above. The primary goal of speech processing is to improve the quality, understanding, and usability of speech signals to facilitate interactions between humans and machines.
Speech recognition, on the other hand, is a subset of speech processing that specifically focuses on converting spoken language into text. It deals with identifying and transcribing phonemes, words, and sentences from audio input. The main objective of speech recognition is accurate transcription and understanding of spoken words. Speech recognition technologies enable functions like voice commands, transcription, or interactive voice response systems. For instance, when a virtual assistant like Siri recognizes and transcribes “What’s the weather today,” that’s speech recognition.
In short, speech processing is the overarching field that encompasses various techniques and applications, while speech recognition is a specialized part of it, focused primarily on understanding and transcribing spoken language.
Optimizing Speech AI With Speech Processing
Speech processing is critical to speech AI systems. It turns raw audio into action and reliable data using the four techniques we discussed above. Speech AI technologies like aiOla are using speech processing methods to help humans and more meaningful interactions with machines through speaking alone.
With aiOla’s speech AI, frontline workers in industries like manufacturing, fleet management, and food safety can complete complex workflows and collect high-level data just by speaking. This technology is optimized by speech processing, which is improving the way we communicate both in professional environments and in our everyday lives.
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