Speech and Data Analytics for Trading Floors: Technologies, Reliability, Accuracy and Readiness

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1 Speech and Data Analytics for Trading Floors: Technologies, Reliability, Accuracy and Readiness Worse than not knowing is having information that you didn t know you had. Let the data tell me my inherent risk and exposure. The challenge is knowing - where does it sit, how can I get my fingers on it and is it the right data? William Langford, global head of compliance architecture and strategy, Citi Karen Winter January 2015

2 Contents Introduction 3 What is Analytics 4 History 4 Technologies Explained 5 Definition of Data Reliability: Accuracy and Completeness 8 Essential Facts and Reliability 9 Results from Customer Tests 11 Summary 11 2

3 Introduction When you produce so much data every day, year in year out, and face more and more demanding regulatory requirements to store; search and piece together your trading activities, selecting the right solution is vital to the success of your project and managing the exposure of your organization. There has been a long-standing debate between the merits of three very different approaches to solving the speech recognition challenge: Direct phrase recognition (not transcription) Speech to text transcription (also known as large vocabulary continuous speech recognition LVCSR) Speech to phoneme transcription (phonetic) In this paper we look at history, research and the trade-off s between each of these three technologies to help you to determine which is the best approach for your compliance needs. We have included opinion from industry expert Dr. Marie Meteer an independent consultant with over 25 years of experience in speech recognition and natural language processing, and independent tests that have been conducted at the University of Colorado and University of California, that studied the performance of these solutions. It is not necessarily about which solution uses the better technology, but more a case of which technology is fit for purpose in your environment. There is far more to a successful speech analytics solution than simply accuracy and trying to get as close to 100% accuracy for word recognition alone. Analytics solutions measurements are made up of precision (accuracy) and recall (detection). Other considerations must include: Minimizing loss of data at the processing stage to enhance accuracy - in transcription methods, at best, there is a 50% loss of data Speed of processing due to data volumes and to minimize server requirements / cost Speed of searching to meet regulator deadlines, and drive efficiencies Vendor capability of working across multi-channels of data sources Vendor capability to work with all voice recorders and trading solutions Vendor experience and understanding of the trading floor environment Vendor experience of trading specific language and slang 3

4 What is analytics? Analytics is the process of taking raw data and converting it into information that is useful to the end user. Data collected is analyzed to answer questions, test hypotheses or disprove theories. For example, to prove or disprove an instance of market abuse. Data mining is a particular data analysis technique that utilizes a logical set of rules to be followed designed to problem solve, known as algorithms, and provide insights within the data. This enables automatic search for rare occurrences, in addition to being able to make specific search queries in the historic data. When algorithms are applied to the data, relationships can be established between different data sets. This means you have the ability to make a link between a recorded conversation and an or chat that confirms a trade has been concluded. Business intelligence is a description that encompasses analysis that utilises aggregation and focuses on business information. History In the late 1990 s, data warehousing was a big growth industry, especially in sectors such as retail, where the widespread usage of customer loyalty cards gathered huge amounts of data about customers, their buying patterns and preferences. Storing the data was never a problem, it was being able to identify and analyze the insightful patterns within these data, through data mining that was difficult. Speech analytics has improved vastly and have, in the case of transcription analytics based solutions, grown up mainly in the call centre environment and sectors where data search is infrequent, such as by legal firms and forensic teams. Direct phrase recognition technology has been around since the 1980 s, however it was too costly relative to its commercial benefit to be commercially available. Since the cost of servers has reduced and the performance of processors has improved, this software became commercially available in the mid 2000 s and Fonetic provided the first implementation of a solution based on direct phrase recognition technology in BBVA s trading floor in There is a strong relationship between the development of business specific language, jargon and slang and the success of an analytics solution in the trading environment. The direct phrase recognition based solution, developed by Fonetic, also utilizes the most developed and advanced banking language model available today. 4

5 Technologies Explained The elements of speech analytics include: Speech engine: a software program that recognizes speech directly for analysis or converts it into data (usually either phonemes sounds that go to make up words or putting phonemes together to make up single words) Indexing layer: a software layer that improves and indexes the output from the speech engine in order to make the data searchable Query and search user interface: the desktop application where users interact with the speech analytics software, defining their requirements and carrying out searches or setting up alerts to enable pro-active searching Reporting applications: the presentation layer of speech analytics, often in graphical format Technologies There are three most used technologies: Direct phrase recognition (non transcription) and two main transcription technologies: Speech to text (also known as large vocabulary continuous speech recognition LVCSR) Speech to phoneme (also known as phonetic) Understanding the benefits and limitations of each of these is essential in relation to the end user environment and purpose for the analytics use. For example, call centre analytics usage and outputs are vastly different to the needs of surveillance and compliance of a bank. Direct Phrase Recognition This technology has a speech engine that directly recognizes and analyses strings of words that make up entire phrases using a language model that combine speech and business context recognition. It processes the audio from the copy of voice recording stored in the voice recorder. The direct phrase recognition engine is uniquely tailored to each specific environment. In the case of the finance sector there is a direct phrase recognition software 5

6 solution that is tailored to banking needs and banking terminology, making it an order of magnitude more reliable at recognizing conversations that are relevant in a banking and trading context than any other solution on the market today. Entire phrases are much better than smaller recognition units of single words or phonemes at compensating for different pronunciation, utterances, other speech irregularities and breaks in speech that are common in everyday speech. The result is a holistic approach to precisely and completely understand and analyze the entirety of a conversation that has taken place between a trader and counterparty with an exponential increase in the data reliability (no loss of data) and value to the financial institution. The processing and first parse of the data is very fast, as are the subsequent searches made by the end user. Pro-active alerts are easy to set up so that calls of concern are delivered to the users dashboard when and where they are needed in the banks workflow. Direct phrase recognition enables banks to analyze 100% of calls and 100% of the call content that can be verified as compliant or without risk, and proven to be so. Transcription technology Apart from Fonetic s solution, that utilizes direct phrase recognition, all other speech analytics solutions use one of the two transcriptions technologies available, these are: Speech to text (LVCSR) Speech to phoneme (phonetic) In both of these technologies a speech recognition engine transcribes recorded calls first and the software transcribes the audio into phonemes (the smallest unit of human speech). Confusingly, each of these transcription technologies are phonetically based. It is the treatment of the phonetic transcription that differentiates them. In both technologies every phoneme is modeled in its phonetic context (e.g. the t between s and r in street is different acoustically than the t at the start of a word, such as, top ). In the speech to phoneme (phonetic) transcription only the possible sequences of sounds and their frequency are considered. In the speech to text (LVCSR) transcription the larger context that the sounds occur in are also taken into account. This gives a degree of compensation where sounds are ambiguous or merge with a neighboring sound (e.g. dish soap). This does help to make this transcription technology more accurate than speech to phoneme (phonetic) transcriptions. Following this process a text mining or search engine attempts to spot keywords, or a combination of key words in the converted text or phonemes. On the face of it this would appear to be a sufficient solution so what are the pit falls? Although transcription analytics enables very fast initial processing the search capability of the speech to phoneme (phonetic) is much slower as the transcriptions cannot be efficiently indexed in the way words and phrases can. It might be able to detect new words but will often find words that might phonetically sound similar to the word being searched and will include a high number of results that are incorrect. The transcription alternative speech to text (LVCSR), is much slower when dealing with high call volumes it is recommended that only sample calls are analyzed. This technology utilizes a standard dictionary of generally 50, ,000 words and is developed for general, not industry specific language. Words that are not in the dictionary will not be recognized. The 6

7 initial processing of the audio takes longer than the speech to phoneme alternative because of the large vocabulary and it requires many more servers to process large volumes of data. In an environment where spot checks provide a trend, such as in a call centre or for marketing purposes this approach is cost effective and adequate. Data loss: Despite the fact that 100% of the calls are processed for analysis there is a permanent loss of data during each of the transcription conversion processes. This is due to the inherent limitations of current speech conversion engines which are also unable to add context to the words in the environment and situation in which they were used. Therefore, keyword spotting is a very manual and inefficient way to approach speech analytics. Due to the data loss, speed of processing and limitations these solutions essentially require banks to settle for sample call checking. In a financially regulated environment where it is essential to detect instances of fraud and understand trader behaviour having limitations of data loss and processing limitations leaves banks exposed. Vendors Compared Fonetic Solutions NICE Verint Direct Phrase Recognition Yes No No Transcription (LVCSR) Yes (in addition) Yes (only) Yes (only) Loss of data before analysis No Yes Yes Trading floor language Yes No No Trading floor implementation Yes No No Languages 84 Limited to 20 Limited to 35 Trade reconstruction Yes Unproven No Multiple trade reconstruction Yes Unproven No Compatible with all voice recorders Yes No No As William Langford, global head of compliance architecture and strategy at Citi said worse than not knowing is having information that you didn t know you had. Let the data tell me my inherent risk and exposure. The challenge is knowing - where does it sit, how can I get my fingers on it and is it the right data? 7

8 Definition of Data Reliability: Accuracy and Completeness According to the US Government Accountability Office 1, data reliability refers to the accuracy and completeness of computer processed data, given the uses they are intended for. In the context of Speech Recognition Analytics: Completeness is measured by the detection rate it is usual for detection to decrease when accuracy increases. Accuracy: Accuracy is defined as the ability of a measurement to match the actual value of the quantity being measured. In speech recognition we can say that accuracy refers to the portion of results that were correctly recognized within a given result set. For example, in a day of trading the compliance officer is asked for a list of conversations that are about swaps. In the list the compliance officer includes ten conversations, nine are about swaps but one of them is about an option. Nine out ten is 90% accuracy. Completeness (Detection rate): Completeness is defined as the state of being complete and entire; having everything that is needed. In speech recognition this is measured by the detection rate which is the number of occurrences of a given event (or word) found, compared to the actual number of occurrences. To go back to the example of the trading conversations, the compliance officer asks a colleague to check the calls again, in the second check they discover that there are 18 conversations about swaps, therefore the detection of nine conversations is a 50% of the data set. In this example it is obvious that data reliability is critical to financial institutions. It is interesting then to note that most analytics vendors neglect to mention detection rates and focus entirely on accuracy. It is a fact that detection rates will significantly diminish in transcription solutions as accuracy rates are increased. Direct phrase recognition is the only technology that is able to maintain a high detection rate at high accuracy levels. 8

9 Essential Facts Transcription Produces Incomplete Data Although speech to text (LVCSR) and speech to phoneme (phonetic) solutions can be tuned to be relatively accurate, their detection rates suffer as a result. Speech Recognition Engine Unit of Speech Recognition Typical Deployed Accuracy Direct Phrase Recognition Speech to Text (LVCSR) Speech to phoneme (phonetic) Phrases Words Phonemes 85-95% 80-85% 80-85% Detection rate (completeness) out of 10 events tuned to the same accuracy Accuracy 80% 80% 80% Detection (completeness) 70% detection 10% detection 5% detection 10 events 9 results 1-2 results 0 results 10 events 7 true events 1 true event 0 true events Reliability Results from Academic Studies: There have been various academic studies that prove that transcription technologies have low data reliability. According to a study at the University of Colorado 2 when used with extracted call centre data, speech to text (LVCSR) is around 40-50% correct at the individual word level. This means that at least half of the actual words spoken are either not recognized or are recognized incorrectly. So when the analysis is made, the results are only of 50% of the original data set at best. Phoneme (phonetic) recognition is at least 10-20% less reliable than speech to text (LVCSR) due to the small size of the recognition unit. For example the English k sounds in the words kit and skill are not identical but they are variants of a single phoneme /k/. Some languages use as few as two phonemes and the English language uses a set of around 45 phonemes. Compared to the many thousands of words and then phrase combinations and context to language it becomes clear why speech to phoneme (phonetic) recognition analyses about 30% of the original data set. In another study performed at the University of California, Berkeley 3, eight leading speech research groups, including Cambridge University, AT&T, Stanford Research Institute (SRI), research & development specialist BBN and John Hopkins University, participated in a National Institute of Standards (NIST) evaluation of telephony speech word recognition. Their results show significantly worse performance at the phoneme level. Considering this compelling research we can conclude that two out of three phonemes are either not reconized, or are reconized incorrectly. In contrast, the direct phrase recognition software is significantly more reliable than speech to text (LVCSR) and speech to phoneme (phonetic) transcriptions when you introduce banking topics and the unique language patterns of traders into the equation the differences become even more dramatic because business topics are usually verbalized in phrases or not even verbalized at all. 9

10 Consider an example when a trader says Hi, how are you? Mine. and the trader means is the price still the same? I m buying. Even with this translation we don t know what financial instrument is being traded or the price at which he/she is buying. But we do know this is the final communication in the conclusion of a trade negotiation. To understand the detail we would need to link this conversation with the trade ticket or a previous conversation. If the surveillance or compliance officer was trying to find this conversation using word search available in speech to text (LVCSR) or speech to phoneme (phonetic) transcription it just wouldn t be found, even if it had been accurately transcribed first. Probability: Let s consider three of the words in the trader conversation above how are you. Let s assume 50% (transcription) reliability for each individual word. If we multiple the probability of correctly recognizing all three words together (50%x50%x50%), we find the probability of correctly recognizing all three words is12.5%, which means that nine times out of 10 the entire phrase will not be correctly recognized. Put in to context of confidence in banking compliance reporting and assessing banking risk, is an error rate of nine times out of ten acceptable? 10

11 Results from Customer Tests Independent verification was the output of side-by-side tests conducted by prospects testing direct phrase recognition and transcription alternatives (speech to text, LVCSR and speech to phoneme, phonetic). The example shows the results of analytics of the same data set analyzed using direct phrase recognition software and a transcription provider Direct Phrase Recognition Transcription Solution 0 This customer test and other studies confirm that direct phrase recognition is five to nine times more reliable than other options. Summary Adherence to regulations, enterprise policies and procedures and the management of risk is a prerequisite to being in the finance sector today. The majority of communications on your trading floor will comply perfectly with regulations. However, the occurrence of even a single non-compliant event, no matter how rare such an occurrence is, could cause extensive litigation and other difficulties and expense for your bank. It is, therefore, mission-critical in such situations to detect and correct every single occurrence of a non-compliant event. Reliable speech analytics data, which is accurate, complete and delivered in its business context, is the key to optimizing the performance and contribution of the compliance department and will provide insight into trading floor performance and behaviours. 11

12 Fonetic Solutions Fonetic delivers business value to banks that is at the opposite end of the scale from the rest of the speech analytics vendors, whose technology is exclusively transcription based. The Fonetic solution is the only analytics solution that incorporates: Direct phrase recognition for analysis of the complete data set Speech to text (LVCSR) technology to help speed up the process of scanning the content of calls and provides additional wordsearching functions Unique indexing that will detect conversations where key words are not mentioned Proactive alerts and trend spotting 84 languages Single and multiple trade reconstruction Designed for the trading floor environment, over 6 years of trading language and slang experience Seamless integration with banks existing infrastructure: Able to work with all voice recorders and data providers Two global customers BBVA and Santander In summary, Fonetic will Bring all of your data into one platform Analyze voice recordings, , text message and instant messages together Provide fast & accurate search capability and easy pro-active alerts 1 US Government Accountability Office: Assessing the Reliability of Computer-Processed Data (Report# GAO G), July Min Tang, Bryan Pellom and Kadri Hacioglu, Call-Type Classification and Unsupervised Training for the Call Center Domain, University of Colorado at Boulder 3 Shuangyu Chang and Steven Greenberg, Linguistic Dissection of Switchboard-corpus Automatic Speech Recognition Systems, University of California, Berkeley 12

13 Madrid Calle Hermanos García Noblejas, 41, 7º Madrid phone: New York 1250 Broadway, 36th Floor NY New York phone: London Suite 6, 43 Bedford Street WC2E 9HA London phone: +44(0)

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