Pangeanic for Speech Datasets
Thanks to our PECAT platform and mobile phone apps, Pangeanic has created a full Speech Data Collection Platform designed to increase the language coverage and accuracy of both speech recognition systems and speech to text systems.
As we humans interface more and more with machines, and with the growing needs of an aging population, speech dataset becomes a crucial component of Artificial Intelligence (AI) systems. With the rise of Natural Language Processing (NLP) technologies, speech recognition has become increasingly important for a wide range of applications, including voice assistants, language translation, and transcription.
What is Speech Data?
Speech data refers to any audio recording of human voices (speech), from monologs to dialogs, scripted or not, that is used to train machine learning (ML) models for speech recognition or other NLP applications. The speech data can come in many different forms, including phone conversations, recorded interviews, podcasts, and more. Just as it has done for parallel corpora for machine translation systems, we at Pangeanic are building speech data sets. Sometimes spontaneous speech, sometimes reading our text parallel corpora.
Speech dataset is typically labeled with metadata that provides additional information about the audio file. This can be the device it was recorded with, length of the recording, accents or regionalisms, Kb, quality, the existence of background noise, file format (mp3, mp4, wav, etc.), the speaker's gender, the topic discussed. We collect metadata because we know metadata is essential for training machine learning models effectively.
How is Pangeanic Speech Data Collected?
We collect speech datasets for AI training in several ways. Mostly, we use our PECAT platform and apps to recruit individuals to record themselves speaking in response to specific text prompts that they can see in our apps.
We also collect spontaneous speech that our team of internal transcribers in Europe and Japan turns into text.
Finally, we enter into agreements to buy small sections of pre-existing audio files that have been made publicly available, particularly in low-resource languages.
Manage your recordings with PECAT
Do you have specific recordings to make?
With Pangeanic, not only can you create, manage and edit datasets hassle-free online with an easy drag-and-drop UI, but you can also monitor how our taskers are performing and recordings are progressing.
All speech data will be exhaustively cleansed and annotated as per your requirements so that your algorithms grow as accurate, as strong and wise as you want!
We are here to help you with
· Speech to Text
· Text to Speech
· Sentiment Annotation for Speech Datasets
Visit our page on Speech Data Annotation for more information on annotation.
Need Speech Datasets? Pangeanic will make them for you
Speech to Text Datasets - Transcription
We've been the preferred transcription vendor for Valencia's Parliament, transcribing hundreds of hours of parliamentary sessions. Our mobile and desktop app allow our taskers to annotate content and context so your Natural Language Processing (NLP) technology improves. You’ll love our mobile app and PECAT platform for speech! We only deliver stock or made-to-order speech data at scale, of high quality and large volumes. Pangeanic offers a 3-month test guarantee and marks delivered content with the type of IP agreement. Pangeanic's speech datasets corpora are collected meticulously and revised - all work is guaranteed and of the highest quality, including labeling homonyms such as “I wrote a letter on the bat” meaning a veterinarian wrote a letter on the nocturnal bird-like animal, not the wooden object used to hit a baseball. Our annotators will consider such cases, domain and context to thwart any possible ambiguity. Banking on 20 years of translation services, Pangeanic is uniquely positioned when it comes to language services: starting from our initial translator database, we have expanded to add thousands of speech taskers worldwide, making sure that only native speakers annotate the text.
Our recordings offer you full ownership and full copyright, both for the audio collected and for the transcription for ML training. Pangeanic follows processes so that Ethical AI is built into every step and you can be sure it is passed on to your products. Our customers enjoy a smooth relationship with a trustable vendor of text-to-speech services supplying Training Datasets to improve ASR performance, freeing you from the hassle of generating, collecting, and processing audio, whilst adding valuable metadata.
Sentiment Annotation for Speech Datasets
Sentiment analysis provides very valuable insights that can often drive business decisions. Pangeanic has a large amount of experience in building its own sentiment analysis tools. To extract sentiment, you must be a native speaker of the language and understand all the nuances and intricacies, for example ironic language. Our recordings can be annotated as positive, negative, or neutral to add more data to train an ML model that will be able to understand and provide its own insights on sentiments. Our PECAT text annotation tool speeds up all sentiment annotation tasks.
Challenges of Working with Speech Datasets
Working with speech datasets presents several challenges that are not typically encountered when working with other types of data. Some of the most significant challenges include:
Variation in speech patterns: People speak at different rates, with different accents, and in different contexts. These variations can make it difficult to train models that can accurately recognize speech in all situations
Noise and interference: Background noise, such as music or other people talking, can interfere with speech recognition. This noise must be filtered out to ensure accurate training, although some clients do request it to remain so that their systems understand to filter it out
Data labeling: Speech datasets must be labeled with metadata that provides information about the language spoken, the speaker's gender, and the topic discussed. This labeling process used to be time-consuming and labor-intensive but thanks to Pangeanic’s PECAT tool, annotation and labeling are becoming much simpler tasks
Best Practices for Working with Speech Datasets
To overcome these challenges, there are several best practices that researchers and developers can follow when working with speech datasets. Our speech team ensures that we:
Collect diverse data: To ensure that Machine Learning models can recognize speech accurately in all situations, it is essential to collect diverse speech data that represents a wide range of accents, languages, and contexts
Use high-quality recordings: Low-quality recordings can make it more challenging to filter out background noise and interference, so it is essential to use high-quality audio recordings and filter out the bad quality ones
Enlist human annotators: While automated tools can help label speech data, human annotators are often better at capturing the nuances of language and can provide more accurate labeling
Speech dataset is a critical component of AI training, particularly for applications that involve natural language processing. While working with speech data presents several challenges, following best practices and using the right tools and resources can help researchers and developers build accurate and effective speech recognition models.