Human-machine-interaction is increasingly ubiquitous as technologies leveraging audio and language for artificial intelligence evolve. For many of our interactions with businesses--retailers, banks, even food delivery providers--we can complete our transactions by communicating with some form of AI, such as a chatbot or virtual assistant. Language is the basis of these interactions and, consequently an essential component to be considered when creating AI.
By combining the two technologies, audio processing and language processing and speech technology businesses can deliver more efficient and personalized customer experiences. This allows human agents to focus their time on more strategic, high-level tasks. The potential ROI has been sufficient to convince many companies to make investments in the technology. As more money is invested, it will lead to more experiments, leading to forward with new developments and best techniques to ensure successful deployments.
Natural Language Processing
Natural Language Processing, or NLP is a subfield of AI which focuses on the teaching of computers to comprehend and interpret human spoken language. It's the base of speech annotation tools, text recognition tools, as well as other applications of AI that let humans converse engage with machine. With NLP employed as an aid in these scenarios, the models are able to understand human behavior and react in a way that is appropriate, opening up huge opportunities in a wide range of industries.
Audio and Speech Processing
For machine learning purposes, the analysis of audio may encompass a range of tools that include automatic speech recognition, music information retrieval and auditory scene analysis to aid in anomaly detection, and many more. Models are commonly utilized to distinguish between sound and speakers and to segment audio files in accordance with classes or by obtaining audio files that are based on similar content. You can also use speech and transform it into text in a matter of minutes.
Speech Dataset needs some preprocessing steps, which include digitization and collection, before being analysed by an algorithm for ML.
Audio Collection and Digitization
In order to begin the audio processing AI project, you'll require an abundance in high-quality information. If you're training virtual assistants voice-activated search features and other project for transcription, then you'll require specific speech data that can be used to cover the scenarios you need to cover. If you're not able to locate what you're seeking, you might need to develop your own or partner with a service such as GTS to get the data. This could include roles-plays, scripted responses, and even spontaneous conversations. For instance, when training a virtual assistant , such as Siri or Alexa you'll need audio recordings of every command you might expect your user to provide an assistant. Other audio projects may require non-speech audio excerpts for example, like cars driving through or children playing according to the purpose.
Audio Annotation
When you've got the audio data to suit your needs It is necessary to make notes on the data. For the audio process, it typically involves dividing the audio into speakers, layers and timestamps when needed. You'll probably need an array of human labelers to complete this tedious annotation task. If you're working with data from speech it is essential to find annotators who have a good command of the required languages, and sourcing from a global source is a good option.
Audio Analysis
If your data is complete then you can use one of the many methods to analyse it. For illustration, we'll present two well-known methods of extracting data:
Audio Transcription, or Automatic Speech Recognition
One of the most commonly used types of audio processing transcription or Automatic Speech Recognition (ASR) is extensively used in all industries to enhance interactions between technology and humans. The purpose to use ASR is to translate spoken words into text using NLP models to ensure precision. Before ASR existed, computers only record the highs and lows in our speech. Today, algorithms can recognize the patterns of audio recordings, and match them to the sounds of different languages, and figure out what words the speaker used.
An ASR system can include a variety of techniques and tools that produce text output. In most cases, these two types of models are used:
- Acoustic Model: Turns sound signals into phonetic representations.
- Model of language: Maps possible phonetic representations to the words and sentence structure that represent the language of the given.
ASR depends heavily upon NLP to create precise transcripts. Recently, ASR has leveraged neural networks that are used in deep learning to produce output that is more precise and with less supervision needed.
ASR technology can be evaluated on the basis of it's accuracy, which is measured in terms of word error rate and speed. The objective for ASR is to reach the same level of accuracy as human listening. However, there are still challenges in navigating the various dialects, accents, and pronunciations, aswell in removing background noise effectively.
Audio Classification
Audio input can be extremely complicated, particularly if many kinds of sounds are contained in one. For instance, at the dogs' park, one might hear conversations, birds chirping, dogs barking and cars passing through. Audio classification can help solve this issue by separating audio categories.
The task of determining the audio quality begins with an AI Data Annotation and manual classification. Teams will then find important features from audio inputs and use a classification algorithm to sort and process them.
Real-Life Applications
Solutions to real-world business issues using audio, speech and language processing could result in improvements to customer experience reduce costs, speed up the process and time-consuming human labor and help focus attention on higher-level corporate processes. Solutions in this area are prevalent every day. Examples of these solutions are:
- Chatbots and virtual assistants
- Search functions that are activated by voice
- Text-to-speech engines
- The car's commands
- Transcribing meetings or calls
- Security enhanced with voice recognition
- Phone directories
- Translation services
Where did the data come from?
IBM's first work in the area of voice recognition was component of U.S. government's Defense Advanced Research Projects Agency (DARPA) Effective Affordable Reusable Text-to-Text (EARS) program that led to major technological advancements in speech recognition. The EARS program produced around 140 hours of controlled BN information for training as well as 9,000 hours of extremely light-supervised training data, derived from closed captions in TV shows. In contrast, EARS produced around 2,000 hours of highly-supervised, human-transcribed training data to train phone conversation (CTS).
It's time to get down to business
In the initial group of tests, the team tested separately their LSTM models and ResNet models with the n-gram and FF NNLM before adding scores from both Acoustic models to compare them to the results of the earlier CTS assessment. Contrary to the results obtained on the initial CTS tests, there was no significant decrease in Word Error Rate (WER) was seen when scores from both LSTM or ResNet models were merged. The LSTM model that has an n-gram LM is quite effective on its own and its results continue to improve when you add the FF-NNLM.
For the second set of experiments, word lattices were generated after decoding with the LSTM+ResNet+n-gram+FF-NNLM model. The team created the n-best lists of these lattices before rescoring them using the LSTM1-LM. The LSTM2-LM also was used to rescore word-lattices independently. The WER performance was significantly improved following the use of LSTM LMS. The researchers were able to speculate that the secondary fine-tuning using the BN's specific information is what allows the LSTM2-LM system to perform better than LSTM1 LM.